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Mathematics & Statistics RIGHTS GUIDE Spring/Summer


Contact: Julie Attrill (

Maths & Stats Rights Guide: Spring/Summer 2013 Probability & Statistics........................................................ 5 Propagation Dynamics on Complex Networks: Models, Methods and Stability/Fu ...................... 5 Introduction to Statistics Through Resampling Methods and R, 2e/Good ..................................... 5 An Accidential Statistician: The Life and Memories of George E.P. Box/Box................................ 6 Information Search After Static or Moving Targets: Theory and Modern Applications/Ben-Gal . 6 The Data Bonanza: Improving Knowledge Discovery in Science, Engineering, and Business/Atkinson ............................................................................................................................... 7 Sample Size Determination and Power/Ryan.................................................................................... 7

Biostatistics and Clinical Trials ......................................... 8 Multiple Imputation and its Application /Carpenter .......................................................................... 8 Design and Analysis of Clinical Trials: Concepts and Methodologies, 3e/Chow .......................... 8 Statistical Modeling in Clinical Trials/Anisimov ................................................................................ 9

Categorical Data Analysis .................................................. 9 Log-Linear Modeling: Concepts, Interpretation, and Application/von Eye..................................... 9

Mathematics ....................................................................... 10 Combinatorics: An Introduction/Faticoni......................................................................................... 10 Introduction to Topology and Geometry/Stahl ............................................................................... 10

Engineering Statistics ....................................................... 11 Statistics and Probability with Applications for Engineers and Scientists/Gupta....................... 11 Multi-criteria Decision Analysis: Methods and Software/Ishizaka ................................................ 11 Problem Solving and Data Analysis Using Minitab: A Clear and Easy Guide to Six Sigma Methodology/Khan ............................................................................................................................ 12 Optimal Redundancy Allocation: With Practical Statistical Applications and Theory/Ushakov. 12

Management Science / Operations Research ................. 13 Game Theory: An Introduction, 2e/Barron....................................................................................... 13 Multicriteria Decision Aid and Artificial Intelligence: Links, Theory and Applications/Doumpos ............................................................................................................................................................ 13 Model Building in Mathematical Programming/Williams ................................................................ 14

Mathematical Modeling ..................................................... 14 Mathematical Modeling with Multidisciplinary Applications/Yang ............................................... 14

Medical Statistics & Epidemiology .................................. 15 Statistical Methods for Hospital Monitoring with R/Morton ........................................................... 15

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Multivariate Analysis ......................................................... 15 Spectral Clustering and Biclustering of Networks: Large Graphs and Contingency Tables/Bolla ............................................................................................................................................................ 15

Optimization ....................................................................... 16 An Introduction to Optimization/Chong ........................................................................................... 16

Quality, Productivity & Reliability .................................... 16 Introduction to Logistics Systems Management/Ghiani ................................................................ 16

Regression Analysis ......................................................... 17 Handbook of Regression Analysis/Chatterjee ................................................................................ 17 Applied Logistic Regression/Hosmer .............................................................................................. 17

Statistics for Finance, Business & Economics .............. 18 Business Analytics and Data Mining with R/Ledolter ..................................................................... 18 Efficiency and Productivity Growth: Modelling in the Financial Services Industry/Pasiouras ... 18 Financial Derivative and Energy Market Valuation: Theory and Implementation in MATLAB速/Mastro .............................................................................................................................. 19 Designing and Conducting Business Surveys/Snijkers ................................................................. 19 Handbook of Decision Analysis/Parnell .......................................................................................... 20

Methods & Statistics in Ecology ...................................... 20 Introduction to Probability and Statistics for Ecosystem Managers: Simulation and Resampling/Haas .............................................................................................................................. 20

Applied Mathematics ......................................................... 21 Applied Diffusion Processes from Engineering to Finance/Janssen ........................................... 21

Linear Algebra.................................................................... 21 Discrete Event Systems in Dioid Algebra and Conventional Algebra/Declerck ........................... 21

Statistics for Finance, Business & Economics .............. 22 Continuous-time Asset Pricing Models in Applied Stochastic Finance/Vassiliou ....................... 22 Introduction to Probability Theory and Stochastic Processes/Chiasson ..................................... 22

Statistics - Operations Research ..................................... 23 Advanced Dynamic-System Simulation: Model Replication and Monte Carlo Studies/Korn...... 23 Business and Scientific Workflows: A Web Service-Oriented Approach/Tan ............................. 23 Product and Systems Development: A Value Approach/Weiss .................................................... 24

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Mathematics ....................................................................... 24 Quantum Dynamics for Classical Systems: With Applications of the Number Operator/Bagarello ............................................................................................................................. 24 Public Key Cryptography: Applications and Attacks/Batten ......................................................... 25

Probability & Statistics...................................................... 25 Spatio-temporal Design: Advances in Efficient Data Acquisition/Mueller.................................... 25 Spatial and Spatio-Temporal Geostatistical Modeling and Kriging/FernĂĄndez-AvilĂŠ ................... 26 Willful Ignorance: The Blind Side of Statistics/Weisberg ............................................................... 26 General Theory of Coherent Lower Previsions/Troffaes ................................................................ 27 An Introduction to Imprecise Probabilities/Coolen ........................................................................ 27 Computational and Statistical Methods for Protein Quantification by Mass Spectrometry/Eidhammer.................................................................................................................. 28 Visual Data Mining: The VisMiner Approach/Anderson .................................................................. 28 The R Book, 2e/Crawley .................................................................................................................... 29 Improving Surveys with Process and Paradata/Kreuter................................................................. 29 Methods and Applications of Statistics in the Atmospheric and Earth Sciences/Balakrishnan . 30 Nonparametric Predictive Inference/Coolen ................................................................................... 30 Categorical Data Analysis 3e/Agresti ............................................................................................... 31 An Introduction to Social Network Analysis with Applications on Organizational Risk/McCulloh ............................................................................................................................................................ 31

Biostatistics and Clinical Trials ....................................... 32 Population-based Cancer Survival Analysis/Dickman ................................................................... 32 Statistical Modelling of ICU Data/Chevret........................................................................................ 32 Understanding Large Temporal Networks and Spatial Networks: Exploration, Pattern Searching, Visualization and Network Evolution/Doreian.............................................................. 33 Applied Missing Data in the Health Sciences/Zhou ........................................................................ 33 Longitudinal Data Analysis, 2e/Hedeker ......................................................................................... 34 How to Design, Analyse and Report Cluster Randomised Trials in Medicine and Health-Related Research/Campbell............................................................................................................................ 34 An Introduction to Adaptive Designs With Applications to Clinical Trials Using R/Chernick..... 35

Statistics for Engineering ................................................. 35 A First Course in Probability and Markov Chains/Modica ............................................................. 35

Statistics for Finance, Business and Economics .......... 36 Handbook of Financial Risk Management/Chan............................................................................. 36 Financial Risk Modelling and Portfolio Optimization with R/Pfaff ................................................ 36

Statistics for the Social Sciences .................................... 37 Applied Time Series Analysis for the Social Sciences: Specification, Estimation, and Inference/Baker.................................................................................................................................. 37 M a t h s


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Research Methods for Postgraduates, 3e/Greenfield ..................................................................... 37

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Probability & Statistics

Introduction to Statistics Through Resampling Methods and R, 2e

Propagation Dynamics on Complex Networks: Models, Methods and Stability

Phillip I. Good 978-1-118-42821-4 / 1-118-42821-8 224 pp. Pub: 22/02/13 General & Introductory Statistics

Xiaoming Fu, Michael Small, Guanrong Chen

This book provides a quick and highly accessible study of an alternative approach to understanding basic statistics.

978-1-118-53450-2 / 1-118-53450-6 320 pp. Pub: 16/08/13 Applied Probability & Statistics

Written in an informal, highly accessible style, this text is an excellent guide to descriptive statistics, estimation, testing hypotheses, and model building. All the pedagogical tools needed to facilitate quick learning are provided, including:

Explores emerging topics of epidemic dynamics on complex networks, including theories, methods, and realworld applications with elementary and wide-coverage, suitable to use as a textbook, as well as a research reference book.

• More than two hundred and fifty exercises, now with selected "hints," scattered throughout the text to stimulate readers' thinking and to actively engage them in applying their newfound skills. • An increased focus on why a method is introduced.

• Provides an introduction to general epidemic models, continuing with models evolving over complex networks and finally present results concerning dynamics of Network-based models on a macroscopic scale.

• Multiple explanations of basic concepts. • Real-life applications disciplines.




• Combines coverage of a wide range of areas from applied mathematics and complex systems to social and biological sciences.

• A companion FTP site, which provides access to all data sets and R programs discussed in the text.

• Serves as a tutorial for experts who work on the surveillance and control of epidemic spreading such as the flu virus.

• Dozens of thought-provoking, problem solving questions in the final chapter to assist readers in applying real-life statistics.

• Covers all basic topics in the field, providing a self-study text for graduate students and researchers interested in network science and engineering.

• An instructor's manual that provides answers to exercises in the book. This text or supplement serves as an excellent introduction to statistics for students in all disciplines. The accessible style and focus on real-life problem solving are perfectly suited for both students and practitioners. It makes for light reading with practitioners of statistics who are eager to learn an alternative approach to understanding basic concepts.

Researchers in the area of computational, mathematical and biological sciences. Graduate students in the fields of applied mathematics and mechanics, electrical engineering, computer science, information science, communication networks, applied physics, biological and life sciences, as well as biomedical and social sciences.

Phillip I. Good is Operations Manager of Information Research, a consulting firm specializing in statistical solutions for private and public organizations. He has published more than thirty scholarly works, more than 600 articles, and fourteen books, including Common Errors in Statistics (and How to Avoid Them) and A Manager's Guide to the Design and Conduct of Clinical Trials, both from Wiley.

Xinchu Fu, Professor, Shanghai University, Shanghai, China. Michael Small, Professor, University of Western Australia, Australia. Guanrong Chen, Chair Professor, City University of Hong Kong, Hong Kong SAR, China.

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An Accidential Statistician: Information Search After The Life and Memories of Static or Moving Targets: George E.P. Box Theory and Modern Applications George E. P. Box Irad Ben-Gal, Eugene Kagan

978-1-118-40088-3 / 1-118-40088-7 306 pp. Pub: 03/05/13 Popular Interest Statistics

978-0-470-97393-6 / 0-470-97393-5 360 pp. Pub: 24/05/13 Data Mining Statistics

This book represents the most accurate accounting of the life, works, and memories of one of the world's most beloved and admired pioneers in experimental design and quality control, Professor George E.P. Box. It offers a narrative of his life, from his early childhood on to his celebrated career in academia and industry.

Provides a new approach unifying the current algorithms relating to the problem of real-time search for a static or moving target. • Provides a general information-theoretic approach to the problem of searching in real-time for a static or a moving target over a discrete sample space.

While many interviews and articles have been published about Box, this memoir is the only first-hand account of his professional accomplishments and personal insights, written in the engaging, charming manner unique to the author. •

• Provides algorithms applicable to practical fields such as military sensor-based searches, location management in cellular and computer networks, and efficient decision trees in data mining applications.

Celebrating his countless accomplishments and impact on the statistics and engineering communities, more than a dozen researchers and practitioners provide their thoughts and accounts of how Box touched their careers and lives.

• Extremely relevant for classification of data records. • Presents a unified framework for the methods currently available and demonstrates their integration.

The author's stories and personal insights are accompanied by numerous, previously unpublished photos from Box's personal collection.

• The algorithms discussed will be implemented in C++ programs, available on an accompanying website. R&D engineers and researchers in the field of data mining and knowledge engineering. Graduate students in engineering and applied mathematics.

General readership. George E.P. Box, PhD, Ronald Aylmer Fisher Professor Emeritus of Statistics & Industrial Engineering, University of Wisconsin--Madison. He is a Fellow of the Royal Society of London, the American Academy of Arts and Sciences as well as an honorary Fellow and Shewart and Deming Medalist of the American Society for Quality and an honorary member of the International Statistical Institute. He is also the recipient of the Samuel S. Wilks Memorial Medal of the American Statistical Association and the Guy Medal in Gold of the Royal Statistical Society. Dr. Box is the coauthor of Statistics for Experimenters: nd Design, Innovation, and Discovery, 2 Edition; Response nd Surfaces, Mixtures, and Ridge Analyses, 2 Edition; Evolutionary Operation: A Statistical Method for Process Improvements; Improving Almost Anything: Ideas and Essays, Revised Edition; Time Series Analysis: th Forecasting and Control, 4 Edition; and Bayesian Interface and Statistical Analyses, all published by Wiley.

Irad Ben-Gal, Associate Professor, Department of Industrial Engineering, Tel-Aviv University, Israel. Eugene Kagan, PhD candidate, Department of Industrial Engineering, Tel-Aviv University, Israel.

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The Data Bonanza: Sample Size Determination Improving Knowledge and Power Discovery in Science, Thomas P. Ryan Engineering, and Business 978-1-118-43760-5 / 1-118-43760-8 384 pp. Pub: 05/07/13 Survey Research Methods & Sampling

Malcolm Atkinson 978-1-118-39864-7 / 1-118-39864-5 580 pp. Pub: 13/05/13 Data Mining Statistics

Featuring a comprehensive approach to sample size determination for general statistical use, this book uniquely blends applications from a variety of fields including statistics, biostatistics, the health sciences, and engineering.

This practical book provides a crucial conceptual framework through a wealth of examples and case studies that develop technical skills in distributed systems, databases, data mining, image processing, time series analysis and more. It integrates aspects of data challenges and addresses the skills needed to design and develop solutions. •

Includes examples and case studies that suggest strategies for addressing a wide variety of data-intensive challenges.

Helps to develop technical skills to design and develop data-intensive methods and processes.

Incorporates an introduction to the current R&D efforts worldwide.

Presents the most up-to-date opportunities challenges emerging in knowledge discovery.

Examples of case studies and applications in other fields.


Academic use, particularly those designing, presenting or attending new advanced courses on data-intensive computing. Also, strategic use by IT strategists and research use for research leaders.

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Presents a modern, general approach to sample size determination and provides extensive literature coverage in addition to considerable guidance on available software.

Fills a gap in the current literature by discussing appropriate software to perform and guide sample size determination including CATD (computer-aided trial design).

Contains coverage of advanced topics, such as multivariate analysis, factor analysis, and cluster sampling.

Thomas P. Ryan, PhD, is a consultant for Cytel Software Corporation as well as the Book Review Editor for the Journal of Quality Technology. He also is currently teaching advanced courses on sample size determination, design of experiments, and engineering statistics at He has acted as a Visiting Professor in the Department of Statistics at the University of Michigan as well as the Director of Statistical Consulting in the Department of Statistics at Case Western Reserve University. He is the author of four previous books, all of which are published by Wiley.

Malcolm Atkinson has over 43 years’ experience of academic service. He is currently professor of e-Science in the School of Informatics, University of Edinburgh, Director of the e-Science Institute and UK e-Science Envoy. He was until recently a member of the JISC Board and the Open Grid Forum Board, as well as many advisory boards and has taken a leading role in five books, all edited collections. He has led research projects continuously since 1978 on data management, programming languages, computational systems and e-Science.


Highlights the applicability of sample size determination by featuring a variety of discussions on broad topics including epidemiology, microarrays, survival analysis, design of experiments, regression, and confidence intervals.

As a reference on useful sampling and estimation methods for scientific researchers and others who use sampling in a variety of fields such as statistics, biostatistics, the health sciences, mathematics, ecology, and geology; as a textbook for an upper-level undergraduate or graduate-level course in statistical sampling; and corporate and academic libraries.

Researchers in companies, governmental organisations and academia who support strategists and decision makers or who develop the tools and services that enable new methods.

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Biostatistics and Clinical Trials

Design and Analysis of Clinical Trials: Concepts and Methodologies, 3e

Multiple Imputation and its Application

Shein-Chung Chow, Jen-Pei Liu 978-0-470-88765-3 / 0-470-88765-6 800 pp. Pub: 15/02/13 Clinical Trials

James Carpenter, Michael Kenward 978-0-470-74052-1 / 0-470-74052-3 368 pp. Pub: 18/01/13 Biostatistics

This new edition provides complete, comprehensive, and expanded coverage of recent health treatments and interventions. Featuring a unified presentation, the book provides a well-balanced summary of current regulatory requirements and recently developed statistical methods as well as an overview of the various designs and analyses that are utilized at different stages of clinical research and development. Additional features include:

A practical guide to the essential statistical tools needed to handle missing data in order from both observational studies and randomized trials. Collecting, analysing and drawing inferences from data is central to research in the medical and social sciences. Unfortunately, it is rarely possible to collect all the intended data. The literature on inference from the resulting incomplete data is now huge, and continues to grow both as methods are developed for large and complex data structures, and as increasing computer power and suitable software enable researchers to apply these methods.

• Features five new chapters on the following topics: biomarker development and target clinical trials, adaptive design, trials for evaluating diagnostic devices, statistical methods for translational medicine, and traditional Chinese medicine. • Contains new sections on bridging studies and global trials, QT studies, multinational trials, comparative effectiveness trials, and the analysis of QT/QTc prolongation.

This book focuses on a particular statistical method for analysing and drawing inferences from incomplete data, called Multiple Imputation (MI). MI is attractive because it is both practical and widely applicable. The authors aim is to clarify the issues raised by missing data, describing the rationale for MI, the relationship between the various imputation models and associated algorithms and its application to increasingly complex data structures.

• Provides a well-balanced overview of current and emerging clinical issues as well as newly developed statistical methodologies. • Focuses on the interaction between clinicians, biostatisticians, and other clinical scientists during the various phases of clinical research and development.

• Provides an introduction to general multiple imputation methods. • Discusses issues that arise with the use of MI in practical settings and recent developments

• Incorporates actual trials in order to feature everyday applicability and explains concepts through illustrations and numerous real examples from clinical trials.

• Features a number of detailed case studies that show how the techniques can be applied in practice. • Illustrates the use of MI in SAS, Stata, WinBUGS, MLwiN and R.

• Fills a major void between clinical and statistical disciplines and provides a complete and balanced presentation of clinical and scientific issues, statistical concepts, and methodologies.

• A supplementary website will host the relevant datasets and computer code.

Reference, research and graduate course book. Shein-Chung Chow, Professor, Dept of Biostatistics and Bioinformatics, Duke University. Jen-Pei Liu, PhD, is an Investigator for the National Health Research Institutes in Taipei, Taiwan. Both authors have extensive background experience in industry and academia, and, collectively, they have published well over twenty books in their respective fields of study.

• Written by the leading authorities in the area. Applied statisticians and researchers dealing with missing data problems in the medical and social sciences field. Academics and graduate students working in missing data James Carpenter, Medical Statistics Unit, London School of Hygiene and Tropical Medicine, UK. Michael G. Kenward, Medical Statistics Unit, London School of Hygiene and Tropical Medicine, UK. 8 C o n t a c t :

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Statistical Modeling in Clinical Trials

Categorical Data Analysis Log-Linear Modeling: Concepts, Interpretation, and Application

Vladimir Anisimov, Valerii V. Fedorov 978-1-84821-214-5 / 1-84821-214-3 256 pp. Pub: 05/06/13 Applied Probability & Statistics - Models

Alexander von Eye, Eun-Young Mun

Novel statistical techniques for predictive analytic modelling patient recruitment and drug supply in clinical trials.

978-1-118-14640-8 / 1-118-14640-9 472 pp. Pub: 04/02/13 Categorical Data Analysis

Clinical trials at the confirmatory stage of drug development typically require a large number of patients recruited by many clinical centers. The design of multicenter clinical trials consists of several interconnected stages including patient recruitment planning, choosing a randomization scheme and a statistical method for analyzing patient responses on different treatments, and planning the drug supply to satisfy patient demand in different centers and regional depots. This book concentrates on the discussion of the novel analytic statistical techniques for modeling and predicting patient recruitment, randomization and drug supply processes in multi-center clinical trials, provides practical recommendations and many examples using real-life case studies and simulation in R. •

Utilizing real-world examples along with R and SYSTAT software, this book presents the multidisciplinary applications of new and advanced methods, models, and applications of log-linear modeling. Highlighting advances that have lent to the topic's distinct, coherent methodology over the past decade, it provides an essential, introductory treatment of the subject, featuring many new and advanced log-linear methods, models, and applications. The book begins with basic coverage of categorical data, and goes on to describe the basics of hierarchical log-linear models as well as decomposing effects in cross-classifications and goodness-of-fit tests. • The generalized linear model (GLM) along with popular methods of coding such as effect coding and dummy coding.

Provides a description of the innovative statistical techniques for predictive analytic modelling patient recruitment, randomization and drug supply processes in multicentre clinical trials.

• Parameter interpretation and how to ensure that the parameters reflect the hypotheses being studied.

The implementation of the software tools based on the developed techniques completely changed the way of planning/monitoring patient recruitment and supply chain in GlaxoSmithKline and led to huge cost savings and substantial benefits on company level.

• Symmetry, rater agreement, homogeneity of association, logistic regression, and reduced designs models. Graduate students, researchers and applied statisticians. Alexander von Eye, Professor of Psychology, Michigan State University, is also Chair, Developmental Psychology Unit, and has over thirty years of academic and research experience at universities across the world and has published ten books and more than 100 journal articles in the areas of statistical methods, categorical data analysis, and cognitive development. A Fellow of the Association for Psychological Science, Dr. von Eye serves as Section Editor on Categorical Data Analysis for the Encyclopedia of Statistics in Behavioral Science (Wiley, 2006). Eun-Young Mun, Assistant Professor of Psychology, Rutgers University. Patrick Mair, PhD, is Assistant Professor at the Institute for Statistics and Mathematics at the Vienna University of Economics and Business (Austria). He is Associate Editor of the Journal of Statistical Software and also serves on the Executive Board of the Austrian Statistical Society. Dr. Mair has published numerous journal articles in the areas of categorical data analysis, latent variable modeling, and computational statistics.

Practitioners. PhD students. Vladimir.V.Anisimov, Professor, Senior Director, Quantitative Sciences, GlaxoSmithKline, has led and coauthored the development and implementation in GSK R&D of the innovative predictive patient recruitment and risk-based drug supply analytic modelling tools.His current research interests include stochastic modelling of multicentre clinical trials, statistical inference, asymptotic methods, optimization, and Bayesian technique. Valerii V. Fedorov, Professor, Group Director, Quantitative Sciences, GSK, lectured as a Visiting Professor of Statistics at the University of Minnesota, USA.

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Introduction to Topology and Geometry

Combinatorics: An Introduction

Saul Stahl 978-1-118-10810-9 / 1-118-10810-8 544 pp. Pub: 08/03/13 Geometry & Topology

Theodore G. Faticoni 978-1-118-40436-2 / 1-118-40436-X 328 pp. Pub: 08/02/13 Combinatorics

Covering over three centuries of innovations in many of the central geometrical disciplines, this Second Edition is the most comprehensive introductory-level presentation of modern geometry currently available.

This book provides a treatment of counting combinatorics that uniquely includes both detailed formulas and proofs and features coverage of derangements, elementary probability, conditional probability, independent probability, and Bayes' Theorem. • Includes plenty of worked exercises in every chapter


• Features a new chapter on the elements of projective geometry, which is useful in achieving a unified image of the field of classical plane geometry. • Presents applications to conic selections and, in a natural way, details the many classical properties of conics, including their well-known optical properties and Archimedes' determination of the area of a segment of a parabola.

proofs, and

• Contains detailed explanations of formulas in order to promote fundamental understanding, as opposed to the rote memorization that many texts on this subject require

• Contains historical notes, which are interspersed with the exposition.

• Promotes mathematical thinking by first considering the presented ideas and seeing proofs before reaching conclusions, enabling readers to understand why the presented material is true

• Provides exercises ranging from the routine to challenging, making the material accessible at varying levels of study. An Instructor's Solutions Manual is also available upon written request.

• Bridges combinatorics and probability and prepares students for more advanced mathematics courses

• Presents fully updated and revised content and continues to provide a logical, yet flexible, organization throughout.

• Includes elementary applications that do not advance beyond the use of Venn diagrams, the inclusion/exclusion formula, the multiplication principal, permutations, and combinations As a text or supplement for discrete and/or finite mathematics courses at the upper-undergraduate level; as a reference for professors, teaching assistants, adjunct instructors, or anyone who wants to learn the various applications of elementary combinatorics; as recommended reading for teachers of mathematics and computer science who would like to convey a better understanding and appreciation of the field and apply it to their students; and academic and public libraries. Theodore G. Faticoni, PhD, is Professor in the Department of Mathematics at Fordham University. He received his PhD in Mathematics from the University of Connecticut in 1981. His professional experience includes 30 research papers in peer reviewed journals and 40 lectures on his research to his colleagues.

As a reference for professionals and/or professors teaching geometry and topology who require a refresher and/or guide to the discipline; as an introductory undergraduate level book for two-semester courses in topology and geometry; as a reference guide for graduate students; and academic libraries. Saul Stahl, PhD, is Professor in the Department of Mathematics at the University of Kansas. In addition to authoring six previous texts and more than thirty papers in the field of geometry, he is also the winner of the Carl B. Allendoerfer Award from the Mathematical Association of America. Emanoil Theodorescu, PhD, is past Assistant Professor at both Northern Illinois University and the University of Iowa. He is currently collaborating with Saul Stahl on numerous projects, including Romanian translations of Dr. Stahl's books as well as the completion of solutions manuals.

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Engineering Statistics

Multi-criteria Decision Analysis: Methods and Software

Statistics and Probability with Applications for Engineers and Scientists

Alessio Ishizaka, Philippe Nemery 978-1-119-97407-9 / 1-119-97407-0 328 pp. Pub: 19/07/13 Engineering Statistics

Bhisham C. Gupta, Irwin Guttman 978-1-118-46404-5 / 1-118-46404-4 832 pp. Pub: 02/04/13 Engineering Statistics

Presents an overview of Multi-Criteria Decision Aid theory, methodology and software. • Provides an overview of MCDA theory, methodology and software.

In addition to coverage of popular statistical techniques, this book discusses design of experiments and response surface methodology and utilizes Minitab(R) and Microsoft Office Excel(R) to illustrate the real-world examples and problems, allowing readers to learn how to apply these software packages to analyze statistical data in their field of interest.

• Presents leading methods in the field of MCDA along with their supporting software, including ELECTRE, AHP and Macbeth. • Features case studies solved with each MCDA as well as tables comparing each method.

• Provides a large number of examples using data encountered in real-world situations to support the presented statistical concepts.

• Supported by a supplementary website with free trial versions of the described software along with worked examples.

• Illustrates how the statistical packages MINITAB® and Microsoft Office Excel® can used to aid in the analysis of various data sets.

Practitioners who apply MCDA in various fields: business, finance and applied mathematics, engineering and purchasing. Researchers and academics in any field applying MCDA/MDM methods.

• Covers an appropriate and understandable level of design of experiments, including randomized block designs, one and two-way designs, Latin square designs, factorial designs, and response surface designs.

Alessio Ishizaka, Strategy and Business Systems, University of Portsmouth, UK and Philippe Nemery, Logistics and Management Mathematics research group, University of Portsmouth, UK

• Suitable for a one- or two-semester calculus-based undergraduate statistics course for engineers and scientists. • Presents material in a flexible way, allowing instructors to pick and choose topics for their particular courses and student enrollment and is supplemented with an Instructor's Manual, lecture slides, and data sets for all examples and homework exercises in Minitab, Excel, and JMP. While the book focuses on Minitab and Excel applications, the author have also provided the JMP data sets on the related website. • Features coverage of data description before probability, which is a unique approach amongst its competitors. Student, practitioner, research. Bhisham C. Gupta, Professor and Chair, Dept of Mathematics & Statistics, University of Southern Maine, has written four books and over thirty peer-reviewed publications. His research interests include design of experiments, statistical quality control, and sampling theory. Irwin Guttman, retired Professor of Statistics, Dept of Mathematics, State University of New York, Buffalo. M a t h s


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Problem Solving and Data Analysis Using Minitab: A Clear and Easy Guide to Six Sigma Methodology

Optimal Redundancy Allocation: With Practical Statistical Applications and Theory

Rehman M. Khan

Igor A. Ushakov

978-1-118-30757-1 / 1-118-30757-7 488 pp. Pub: 22/02/13 Data Analysis

978-1-118-38997-3 / 1-118-38997-2 208 pp. Pub: 19/04/13 Quality, Productivity & Reliability

Problem Solving and Data Analysis using Minitab presents example-based learning to aid readers in understanding how to use MINITAB 16 for statistical analysis and problem solving. Each example and exercise is broken down into the exact steps that must be followed in order to take the reader through key learning points and work through complex analyses. Exercises are featured at the end of each example so that the reader can be assured that they have understood the key learning points.

With an overview of different approaches to optimal resource allocation, from classical Lagrange methods to modern algorithms based on ideas of evolution in biology, this book details the applied methods of optimization, describes various methods of optimal redundancy problem solutions, and demonstrates how they can be solved with numerical examples and statistical methods. • Blends probability and reliability theory, mathematical modeling, and mathematical statistics to solve common real-world optimal reliability problems. • Features numerous statistical methods and algorithms of optimal resource allocation as well as the needed mathematical background and numerical examples. • Describes how optimization is an important part of various fields, from communication and transportation to energy transmission and counter-terrorism protection. • Presents practical thoughts, opinions, and judgments on real-world applications of reliability theory via numerous case studies in areas such as transportation and communication and solves practical problems using mathematical models and algorithms. • Contains many illustrative numerical examples and explanatory figures throughout in addition to numerous exercises with solutions and explanations.

• Provides readers with a step by step guide to problem solving and statistical analysis using Minitab 16 which is also compatible with version 15. • Includes fully worked examples with graphics showing menu selections and Minitab outputs. • Uses example based learning that the reader can work through at their pace. • Contains hundreds of screenshots to aid the reader, along with explanations of the statistics being performed and interpretation of results. • Presents the core statistical techniques used by Six Sigma Black Belts. • Contains examples, exercises and solutions throughout, and is supported by an accompanying website featuring the numerous example data sets.

Students, researchers. Igor Ushakov, PhD, Senior Consultant, Advanced Logistics Development, Tel Aviv, is also a past professor at the University of California, San Diego, George Mason University, The George Washington University, and Moscow Technical University. His areas of expertise include operations research, applied statistics, and probabilistic modeling. Dr. Ushakov has authored 24 books in both English and Russian and over 300 journal articles in reliability engineering, logistics, and quality assurance.

Numerical professionals who want to learn to use Minitab 16 for data analysis, problem solving and process improvement. Undergraduate or postgraduate level for academic courses incorporating. Six Sigma Green and Black Belts, from organisations that use Minitab. Rehman M. Khan, Chartered Chemical Engineer and Six Sigma Black Belt, Loughbourough, UK

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Management Science / Operations Research

Multicriteria Decision Aid and Artificial Intelligence: Links, Theory and Applications

Game Theory: An Introduction, 2e

Michael Doumpos, Evangelos Grigoroudis

E. N. Barron

978-1-119-97639-4 / 1-119-97639-1 368 pp. Pub: 15/03/13 Management Science / Operations Research

978-1-118-21693-4 / 1-118-21693-8 544 pp. Pub: 03/05/13 Management Science / Operations Research

Presents recent advances in both Multiple criteria decision aid and Artificial intelligence.

This Second Edition features an authoritative and quantitative approach to modern game theory and provides applications from diverse areas including economics, political science, military science, and finance.

Organisations often face complex decisions requiring the assessment of large amounts of data. In recent years Multicriteria Decision Aid (MCDA) and Artificial Intelligence (AI) techniques have been applied with considerable success to support decision making in a wide range of complex real-world problems.

• Features two new chapters on repeated, recursive, and stochastic games as well as dynamic and extensive games, which includes the use of the freely available software Gambit to model and solve the games.

The integration of MCDA and AI provides new capabilities relating to the structuring of complex decision problems in static and distributed environments. These include the handling of massive data sets, the modelling of ill-structured information, the construction of advanced decision models, and the development of efficient computational optimization algorithms for problem solving. This book covers a rich set of topics, including intelligent decision support technologies, data mining models for decision making, evidential reasoning, evolutionary multiobjective optimization, fuzzy modelling, as well as applications in management and engineering.


• Utilizes Maple and Mathematica® software to find the values and strategies of games, includng both zero and nonzero sum, and noncooperative and cooperative. The command codes are included in the book and are available as worksheets via the book's related Web site. • Thoroughly classroom-tested for over five years and includes extensive examples illustrating game theory's wide range of relevance, this classroom-tested book. • Includes new example exams and quizzes at the end of each chapter, exercises at the end of each section, and select answers in an appendix.

• Covers all of the recent advances in intelligent decision making.

• Provides some proofs for important results, algorithms for the solution of the games, and interesting applications to illustrate the theory.

• Includes a presentation of hybrid models and algorithms for preference modelling and optimisation problems.

Intended as a mathematical introduction to the basic theory of games and is appropriate as a textbook for junior and senior level undergraduate courses in game theory for students majoring in mathematics, economics, engineering, operations research, and computer science; and academic libraries.

• Provides illustrations of new intelligent technologies and architectures for decision making in static and distributed environments. • Explores the general topics on preference modelling and learning, along with the coverage of the main techniques and methodologies and applications.

Emmanuel N. Barron, PhD, is Professor of Mathematical Sciences in the Department of Mathematics and Statistics at Loyola University Chicago. He is the author of over fifty journal articles, and his teaching experience includes optimal control, stochastic processes, differential games, analysis, operations research, game theory, and financial mathematics, to name a few. Dr. Barron received his PhD in Mathematics from Northwestern University in 1974.

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Researchers, academics, and post-graduate students in the fields of operational research, artificial intelligence, management science, decision analysis. Practitioners in business organizations interesting in applying new tools to multiple criteria decision problems. Michael Doumpos and Evangelos Grigoroudis, Technical University of Crete, Department of Production Engineering and Management, Greece.

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Model Building in Mathematical Programming

Mathematical Modeling Mathematical Modeling with Multidisciplinary Applications

H. Paul Williams 978-1-118-44333-0 / 1-118-44333-0 424 pp. Pub: 22/02/13 Management Science / Operations Research

Xin-She Yang 978-1-118-29441-3 / 1-118-29441-6 592 pp. Pub: 25/01/13 Mathematical Modeling

Update to a popular textbook illustrating the scope and limitations of models building in mathematical programming.

Lead by a well-known scholar in the field and written by leading international experts, this book details the interdisciplinary nature of mathematical modeling and numerical algorithms and combines a variety of applications from diverse fields to illustrate how the methods can be used to model physical processes, design new products, find solutions to challenging problems, increase competitiveness in international markets.

• A long awaited update of a popular textbook. • Discusses the general principles of model building in mathematical programming and its applications. • Provides extensive coverage of modelling alternatives. • New material on modeling non-linear statistics. • Updated practical problems to be modeled and solved.

• Provides worked examples, exercises with select solutions, and detailed references of the latest literature to solidify comprehensive learning. The worked examples are appropriate for self-study as well as university courses.

• New chapters and sections explore the latest developments in stochastic programming, constraint programming and column generation as well as modeling languages. • Now includes biological systems problems. • Now accompanied by a supporting website featuring solutions and examples of programming languages such as GAMs and AMPL.

• Presents case studies and real-world applications that are widely used for current mathematical modeling courses, such as the greenhouse effect and Stokes flow estimation.

Undergraduates, postgraduates, research students and managers in Operational Researchscience. Engineers, mathematicians, and scientists who develop/use mathematical programming models.

• Contains comprehensive coverage of a wide range of contemporary topics, such as nonlinear PDEs, game theory, statistical models, and analytical solutions to numerical methods.

H.Paul Williams, Emeritus Professor of Operational Research, Department of Management, London School of Economics, UK.

• Focuses on new techniques and applications and presents balanced coverage of PDEs, discrete models, statistics, fractional calculus, numerical methods, applications, and case studies. Upper-undergraduate and resource for professionals.




Xin-She Yang, PhD, is Senior Research Scientist in the Department of Mathematical and Scientific Computing at the National Physical Laboratory in the United Kingdom. He has a multidisciplinary background and experience in computational mathematics, metaheuristic algorithms, numerical analysis, and engineering optimization. Dr. Yang is the Editor-in-Chief of the International Journal of Mathematical Modeling and Numerical Optimization, a Member of the Society for Industrial and Applied Mathematics and the British Computer Society, and a Fellow of the Royal Institution. He is the author of seven additional books and over 100 journal articles. 1 4 C o n t a c t :

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Medical Statistics & Epidemiology

Multivariate Analysis Spectral Clustering and Biclustering of Networks: Large Graphs and Contingency Tables

Statistical Methods for Hospital Monitoring with R Anthony Morton, Kerrie L. Mengersen, Geoffrey Playford, Michael Whitby

Marianna Bolla

978-1-118-59630-2 / 1-118-59630-7 320 pp. Pub: 12/07/13 Medical Statistics & Epidemiology

978-1-118-34492-7 / 1-118-34492-8 225 pp. Pub: 26/07/13 Multivariate Analysis

This book provides much-needed guidance on data analysis with R for the growing number of scientists in hospital departments who are responsible for producing reports but who have limited statistical expertise.

Bridges the gap between graph theory and statistics, presenting and comparing a wide variety of statistical methods applicable to large networks or network sequences

• This book provides much-needed guidance on data analysis with R for the growing number of scientists in hospital departments who are responsible for producing reports but who have limited statistical expertise.

• Provides a timely, novel and unified treatment of many important problems surrounding the spectral and classification properties of networks. • Examines all new developments in the field of spectral clustering at the interface of statistics, computer science and complexity theory.

• Presents easy to use functions. • Reduces the perceived complexity of R for nonstatisticians.

• Includes practical examples from social and biological networks

• Covers issues that are going to be of increasing importance in the future such as generalised additive models, and complex systems, networks and power laws.

• Presents a collection of possible algorithms based on different distances, matrices, and criteria as well as presenting them in a unified statistical way, to adapt them to real-life situations of statistical learning.

Scientists in hospital departments who are responsible for producing reports, and who are involved in improving safety. Professionals working in the health care quality and safety community.

• Features a supporting website containing a teacher's guide. Academics working on statistical learning, data mining, networks, and multivariate statistics, and practitioners in bioinformatics, sociology, or industry, working with microarrays. Graduate and PhD students learning networks, applied multivariate statistics, and graph theory. Computer scientists and applied graph theorists.

Anthony Morton, Infection Management Services, Princess Alexandra Hospital, Brisbane, and Adjunct Professor, Queensland University of Technology, Brisbane, Australia. Kerrie Mengersen, Research Professor of Statistics, Faculty of Science and Technology, Queensland University of Technology, Brisbane, Australia. Geoffrey Playford, Director Infection Management Services, Princess Alexandra Hospital, Brisbane, Australia. Michael Whitby, Professor of Medicine, The University of Queensland, Brisbane, Australia

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Marianna Bolla, Institute of Mathematics, Budapest University of Technology and Economics, Hungary.

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Quality, Productivity & Reliability

An Introduction to Optimization

Introduction to Logistics Systems Management

Edwin K. P. Chong, Stanislaw H. Zak

Gianpaolo Ghiani, Gilbert Laporte, Roberto Musmanno

978-1-118-27901-4 / 1-118-27901-8 640 pp. Pub: 25/01/13 Optimization

978-1-119-94338-9 / 1-119-94338-8 480 pp. Pub: 08/02/13 Quality, Productivity & Reliability


Praise for the 3 Edition: "This book...guides and leads the reader through the learning path...[e]xamples are stated very clearly and the results are presented with attention to detail." (MAA Reviews)

Presents an updated and expanded treatment of quantitative methods for logistics systems planning, organization and control

This new edition explores the latest applications of optimization theory and methods and features the use of MATLAB® to reinforce the discussed algorithms. • Features a new chapter on integer programming as well as expanded coverage on one-dimensional methods and linear matrix inequalities. • Provides an up-to-date, accessible introduction to optimization theory and methods with an emphasis on engineering design. • Includes additional exercises in almost every chapter, all of which have been class-tested. • Modified throughout to include minor improvements to the organization and discussion and reviews basic definitions, notations, and relations from linear algebra, geometry and calculus, followed by unconstrained optimization problems. • Supplemented with a complete Instructor's Manual, which has been updated to include fully worked-out solutions to all new exercises. The Instructor's Manual is available via written request to the Publisher. • Features a related Website that includes the MATLAB Mfiles for implementation of all discussed theory and algorithms.

• Includes new chapters on procurement and the design of automated storage and retrieval systems. • Heavily revised throughout with approx 30% new material, with obsolete content removed and the readability of the book improved. • Features new case studies that demonstrate how the methods can be applied to complex logistics problems. • Each numerical example is accompanied by a data file in order to allow readers to solve all the problems using open-source software. • Illustrates each topic presented with real examples and is supported by a website featuring new exercises and teaching material. Senior undergraduate and graduate students in engineering, computer science and management science. Researchers, practitioners, and students of logistics and supply chain management, in both academia and industry Gianpaolo Ghiani, Universite del Salento, Italy. Gilbert Laporte, Canada Research Chair in Distribution Management, Canada. Roberto Musmanno, Universite della Calabria, Italy.

As a textbook for a one-semester senior undergraduate or beginning graduate course. Researchers. Professionals. Edwin K. P. Chong, Professor of Electrical and Computer Engineering and Professor of Mathematics, Colorado State University. He is a Fellow of the IEEE and is Senior Editor of the IEEE Transactions on Automatic Control. Dr. Chong received the IEEE Control Systems Society Distinguished Member Award in 2010 and the ASEE Frederick Emmons Terman Award in 1998. Stanislaw H. Zak, Professor, School of Electrical and Computer Engineering, Purdue University, is former Associate Editor of Dynamics and Control and the IEEE Transactions on Neural Networks. 1 6 C o n t a c t :

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Regression Analysis

Applied Logistic Regression

Handbook of Regression Analysis

David W. Hosmer, Stanley Lemeshow, Rodney X. Sturdivant

Samprit Chatterjee, Jeffrey S. Simonoff

978-0-470-58247-3 / 0-470-58247-2 516 pp. Pub: 12/04/13 Regression Analysis

978-0-470-88716-5 / 0-470-88716-8 240 pp. Pub: 04/02/13 Regression Analysis

Based on feedback from colleagues, this book presents expanded coverage on random effects models, estimation in the presence of interaction, and fractional polynomials, and discussions on Bayesian logistic regression, likelihood based confidence interval estimates, tests for non-nested models, and multivariable fractional polynomials has been added. It provides updated SAS, STATA, and BUGS computer code throughout to analyze the data sets, and the R language has also been added to this new edition

The focus of the book is on the tools that both practitioners and researchers use in real life. It is intended to be a comprehensive collection of the theory, methods, and applications of regression methods, but it has been deliberately written at an accessible level. The handbook provides a quick and convenient reference or "refresher" on ideas and methods that are useful for the effective analysis of data and its resulting interpretations. Students can use the book as an introduction to and/or summary of key concepts in regression and related course work (including linear, binary logistic, multinomial logistic, count, and nonlinear regression models). Theory underlying the methodology is presented when it advances conceptual understanding and is always supplemented by hands-on examples.

It also features many new data sets to illustrate the presented methods as well as provide data for the exercises, including data from a number of timely and interesting studies that can be used for case-control, multinomial, and ordinal models • Written at an accessible level with the statistical model developed from a regression analysis point of view.

References are supplied for readers wanting more detailed material on the topics discussed in the book. R code and data for all of the analyses described in the book are available via an author-maintained website.

• Contains new and updated exercises in all chapters • Supplemented with a companion FTP site that includes several data sets to accompany the book's examples and exercises.

• An extensive, up-to-date reference list is provided at the end of each chapter for further research and internal linking (within the book) purposes.

The authors assume that readers have a solid foundation in linear regression methodology and contingency table analysis through Mantel-Haenszel methods.

• More than 250 theories, methods, and applications are showcased throughout in a format that is easy to use.

David W. Hosmer, Professor Emeritus of Biostatistics, School of Public Health (SPH) & Health Sciences, University of Massachusetts. Stanley Lemeshow, Dean, College of Public Health, Ohio State University, is also currently Director of the Summer Program in Applied Biostatistical and Epidemiological Methods, which is held each year at OSU. Dr. Lemeshow is a Fellow of both the American Association for the Advancement of Science and the American Statistical Association. Rodney X. Sturdivant, PhD, is Founding Director of the Center for Data Analysis and Statistics at the United States Military Academy at West Point, NY. He is also Associate and Academy Professor in the Department of Mathematical Sciences. Colonel Sturdivant received his PhD in biostatistics from the University of Massachusetts at Amherst in 2005.

• Mathematical symbolism is repressed as much as possible so as not to overburden the reader with needless theory or confusing notation. Graduate students, researcher, professional. Samprit Chatterjee, Professor, Health Policy, Mount Sinai School of Medicine, Chicago, has held previous academic appointments in over a dozen schools worldwide. On two occasions, he was awarded a Fulbright Fellowship. He has published over 200 journal articles in areas such as statistical methodology, biostatistics and public health, and regression. He is the author or co-author of four books with Wiley, and he is an active Fellow of the American Statistical Association.

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Statistics for Finance, Business & Economics

Efficiency and Productivity Growth: Modelling in the Financial Services Industry

Business Analytics and Data Mining with R

Fotios Pasiouras 978-1-119-96752-1 / 1-119-96752-X 312 pp. Pub: 26/04/13 Statistics for Finance, Business & Economics

Johannes Ledolter 978-1-118-44714-7 / 1-118-44714-X 392 pp. Pub: 14/06/13 Statistics for Finance, Business & Economics

Explores contemporary research issues and the state-of-the art techniques in the area of efficiency and productivity measurement in the financial sector

This book explores real-world applications from the field of business using R, showcasing the software's powerful capabilities for analyzing and exploring large sets of data.

• Authoritative introduction to efficiency and productivity analysis.

• Adapted from the author's own MBA-level course on data mining.

• Explores state-of-the art techniques as well as bringing new research to a field of current critical importance globally.

• Showcases the powerful computing capabilities of R for executing data mining of large data.

• Includes applications from the insurance industry and financial sectors, such as efficiency of Asian banks, cooperatives and not-for --profit credit associations.

• Supplies readers with all of the book's data sets and related R code via a related Web site, allowing readers to easily access the discussed data and carry out their own analyses.

• Discusses the most suitable approach to select inputs and outputs.

• Provides extensive exercises via the book's related Web site, allow readers to test their comprehension of the presented techniques.

• Authored by global experts in the field, including, Loretta Mester, Executive Vice President & Director of Research, Federal Reserve Bank of Philadelphia, USA and Robert DeYoung, Capitol Federal Professor in Financial Markets and Institutions, USA.

As a book for courses on data mining and business statistics at the graduate and MBA level. Johannes Ledolter is Professor in both the Department of Statistics and Actuarial Science and the Department of Management Sciences at the University of Iowa. He is a Fellow of the American Statistical Association and a member of the International Statistical Institute. He has published extensively in his areas of research interest, which include data mining, statistical applications in business, forecasting, international business education, applied time series analysis, and statistical methods for quality and productivity improvement (Six Sigma). Dr. Ledolter is the coauthor of Statistical Methods for Forecasting, Achieving Quality Through Continual Improvement, Statistical Quality Control: Strategies and Tools for Continual Improvement, all published by Wiley. A Fellow of the American Society for Quality, American Statistical Association, and International Statistical Institute.

Academics, practitioners and PhD students/researchers working on the efficiency of financial institutions. Graduate students and researchers in finance. Fotios Pasiouras, Department of Production Engineering and Management, Technical University of Crete, Greece

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Financial Derivative and Energy Market Valuation: Theory and Implementation in MATLAB®

Designing and Conducting Business Surveys Ger Snijkers, Gustav Haraldsen, Jacqui Jones, Diane Willimack 978-0-470-90304-9 / 0-470-90304-X 608 pp. Pub: 01/03/13 Survey Research Methods & Sampling

M. Mastro 978-1-118-48771-6 / 1-118-48771-0 656 pp. Pub: 02/04/13 Statistics for Finance, Business & Economics

This book provides a coherent overview of the business survey process, from start to finish. It uniquely integrates an understanding of how businesses operate, a total survey error approach to data quality that focuses specifically on business surveys, and sound project management principles. It brings together what is currently known about planning, designing, and conducting business surveys, with producing and disseminating statistics or other research results from the collected data. This knowledge draws upon a variety of disciplines such as survey methodology, organizational sciences, sociology, psychology, and statistical methods. The contents of the book formulate a comprehensive guide to scholarly material previously dispersed among books, journal articles, and conference papers.

With an introduction to the needed mathematical financial theory, this book presents statistical and quantitative methods to implement and apply state-of-the-art financial models. • Establishes the fundamental mathematics needed and details all necessary statistical, quantitative, and financial theory. • Details the necessary steps for implementation of the models in MATLAB., which allows for a focus on the relevant quantitative and financial concepts.

It provides guidelines that will help the reader make educated trade-off decisions that minimize survey errors, costs, and response burden, while being attentive to survey data quality.

• Presents a thorough discussion of the affine transform formalism and provides an elegant framework to augment jumps to the two-factor models or develop similar jump-diffusion models.

Major topics include: Determining the survey content, considering user needs, the business context, and total survey quality; Planning the survey as a project; Sampling frames, procedures, and methods; Questionnaire design and testing for self-administered paper, web, and mixedmode surveys; Survey communication design to obtain responses and facilitate the business response process; Conducting and managing the survey using paradata and project management tools.

• Provides all MATLAB code via the book's related website for additional implementation as well ease of use when exporting to other software environments including C++ and C#. • Focuses on developing and utilizing the Kalman filter, which features an optimal recursive solution with very little computational burden for linear models with Gaussian noise.

Researchers, graduate students.

• Exploits the extended, Gauss-Hermite, unscented, Monte Carlo, and particle Kalman filters for the nonlinear and non-Gaussian models developed throughout.

Ger Snijkers, Professor of Business Survey Methodology, Utrecht University and Senior Researcher in Business Survey Data Collection Methodology, Statistics Netherlands. Gustav Haraldsen, from Statistics Norway. Jacqui Jones, from UK Office of National Statistics Diane Willimack, Chief of the Economic Statistical Methods and Programming Division at the US Bureau of the Census.

As a course book for graduate level courses in quantitative finance, mathematical finance, and financial engineering; as a resource for practitioners in the quantitative finance industry; as a reference for industry professionals working in a financial position within the commodity space; and academic and corporate libraries. Michael Mastro, PhD, is a civilian Staff Scientist at the U.S. Naval Research Lab. Dr. Mastro has authored over 130 papers, patents, and journal proceedings and has organized and edited proceedings for four MRS conference symposia.

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Handbook of Decision Analysis

Methods & Statistics in Ecology

Gregory S. Parnell, Terry Bresnick, Steven N. Tani, Eric R. Johnson

Introduction to Probability and Statistics for Ecosystem Managers: Simulation and Resampling

978-1-118-17313-8 / 1-118-17313-9 432 pp. Pub: 15/03/13 Decision Sciences With a focus on the philosophy, knowledge, science, and art of decision analysis, this book integrates single- and multi-objective decision analysis and presents multiple qualitative and quantitative techniques for each key decision analysis task.

Timothy C. Haas 978-1-118-35768-2 / 1-118-35768-X 312 pp. Pub: 28/05/13 Methods & Statistics in Ecology

• Features coverage of soft skills such as behavioral decision making insights, decision framing, and collaboration with stakeholders, all of which are vital for the successful applications of decision analysis. Soft skills are typically overlooked in decision analysis training materials.

An inclusive beginner's guide to solving real-world environmental problems illustrated with applications in R • Presents basic frequentist probability ideas as well as introducing the use of software-based simulation using R.

• Discusses the various challenges of decision makers and presents solutions to key challenges with both single and multiple objective decision analysis techniques.

• Looks at the basics of Bayesian Belief Networks (BBN) and influence diagrams and Simulated Minimum Hellinger Distance parameter estimation.

• Provides a balanced perspective between several schools of practice of decision analysis, serving as a comprehensive resource for practitioners.

• Addresses resampling-based hypothesis tests and confidence intervals that can be applied to spatiotemporal data.

• Includes many examples throughout to illustrate the methods and approaches used to deal with unique challenges related to the field.

• Explores learning algorithms for influence diagrams as well as Individual Based Models (IBMs) of wildlife populations.

• Emphasizes applied decision analysis and is ideal for decision analysis practitioners who would like to increase the breadth and depth of their knowledge.

• Introduces spatial statistics as well as providing a step-bystep guide to installing and using Quantum GIS. • Supported by an online intelligent tutoring system, featuring R code and data used throughout the book, along with tutorials and solutions

• Fills a void in the current decision analysis literature by providing a balanced treatment of soft skills (decision making insights and presentation techniques) and hard skills (decision analysis techniques, mathematics, and modeling).

Ecosystem managers and social scientists. Graduate students in the field of environmental science. Timothy C. Haas, Lubar School of Business Administration, University of Wisconsin at Milwaukee is involved in teaching undergraduate and graduate courses in statistical methods, pursuing decision making and environmental statistics re­search, and collaborating with faculty on application of statistics to Marketing and Economics.

Reference/professional. Gregory S. Parnell, Professor, Dept of Systems Engineering, US Military Academy, West Point, NY, is also Executive Principle Analyst with Innovative Decisions, Inc., a leading decision analysis firm. Terry Bresnick, CEO, Innovative Decisions, Inc. and President of Innovative Decision Analysis, Inc. has extensive experience in applying decision analysis to complex problems of government and industry. Steven N. Tani, is Partner and Fellow of Strategic Designs Group. Eric R. Johnson, Associate Director of Bristol-Myers Squibb..

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Applied Mathematics

Linear Algebra

Applied Diffusion Processes from Engineering to Finance

Discrete Event Systems in Dioid Algebra and Conventional Algebra

Jacques Janssen

Philippe Declerck

978-1-84821-249-7 / 1-84821-249-6 416 pp. Pub: 17/04/13 Applied Mathematics

978-1-84821-461-3 / 1-84821-461-8 176 pp. Pub: 14/12/2012 Linear Algebra

The aim of this book is to promote interaction between Engineering, Finance and Insurance, as there are many models and solution methods in common for solving reallife problems in these three topics. The authors point out the strict inter-relations that exist among the diffusion models used in Engineering, Finance and Insurance. In each of the three fields the basic diffusion models are presented and their strong similarities are discussed. Analytical, numerical and Monte Carlo simulation methods are explained with a view to applying them to get the solutions of the different problems presented in the book. Advanced topics such as non-linear problems, Levy processes and semi-Markov models in interactions with the diffusion models are discussed, as well as possible future interactions among Engineering, Finance and Insurance.

This book concerns the use of dioid algebra as (max, +) algebra to treat the synchronization of tasks expressed by the maximum of the ends of the tasks conditioning the beginning of another task -- a criterion of linear programming. A classic example is the departure time of a train which should wait for the arrival of other trains in order to allow for the changeover of passengers.


Explains analytical, numerical and Monte Carlo simulation methods and describes their applications in relation to problems presented in the book.


Presents advanced non-linear problems, Levy processes and semi-Markov models in interactions with the diffusion models.

The content focuses on the modeling of a class of dynamic systems usually called "discrete event systems" where the timing of the events is crucial. Events are viewed as sudden changes in a process which is, essentially, a man-made system, such as automated manufacturing lines or transportation systems. Its main advantage is its formalism which allows us to clearly describe complex notions and the possibilities to transpose theoretical results between dioids and practical applications. Scientists, researchers and industrial engineers interested in this subject area Philippe Declerck is Senior Lecturer LISA / ISTIA Laboratory at University of Angers, France

This book can be used as reference by heat transfer engineers, financial analysts and high level actuaries. It is a book that can be interesting for researchers in Finance, Insurance and Heat Transfer. Jacques Janssen, Professor, Solvay Business SchoolBrussels. Oronzio Manca, Professor of Thermal Sciences Universita di Roma is a member of Editorial Advisory Boards for The Open Thermodynamics Journal, Advances in Mechanical Engineering, Associate Editor of ASME Journal of Heat Transfer and Journal of Porous Media, and co/author of over 300 scientific papers (81 on peer reviewed journals). Raimondo Manca, Professor, Mathematical Methods for Economics, Finance & Actuarial Science. He has written more than 100 papers of which 38 are published on peer reviewed journals and 4 scientific books.

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Statistics for Finance, Business & Economics

Introduction to Probability Theory and Stochastic Processes

Continuous-time Asset Pricing Models in Applied Stochastic Finance

John Chiasson 978-1-118-38279-0 / 1-118-38279-X 1000 pp. Pub: 20/05/13 Applied Mathematics in Engineering

P. C. G. Vassiliou

This comprehensive textbook provides an introduction to statistical methods for graduate engineers. It presents a thorough coverage of important probability-related topics to aid engineers in product and system design, reliability engineering, quality control and more.

978-1-84821-159-9 / 1-84821-159-7 608 pp. Pub: 19/06/13 Statistics for Finance, Business & Economics

• Provides manual.

Stochastic finance and financial engineering have been rapidly expanding fields of science over the past four decades, mainly due to the success of sophisticated quantitative methodologies in helping professionals manage financial risks. These two volumes aim to provide a foundation course on applied stochastic finance.




• Covers topics ranging from Coin Tossing to Simulation of Random Phenomena. • Includes coverage of Brownian Motion and White Noise, as well as Kalman Filtering. Graduate level engineering students; high-level undergraduate students in electrical engineering. Upperlevel undergraudate and graduate students in Mathematics. John Chiasson began his research into nonlinear methods of control and their application to electric machines. This in turn led to his teaching a beginning graduate level course on control of electric drives, and later a course on control of electric mashines based on earlier manuscripts of the book. He is currently in the Department of Electrical and Computer Engineering at Boise State University. He has authored or coauthored several journal and conference papers in the area of control of electric machines, and is currently an associate editor of the IEEE Transactions on Control Systems Technology.

• Focuses on continuous time models by presenting the necessary material from continuous martingales, measure theory and stochastic differential equations as models for various assets, such as the Wiener process, Brownian motion, etc. • Guides the reader into the pricing of vanilla options in continuous time i.e. the continuous time models of Black and Scholes, followed by interest rate models and the models of Heath-Jarrow-Morton and the forward Libor model. Upper undergraduate/graduates. Reference. P.C.G. Vassiliou, Professor, Mathematics Dept, Aristotle University of Thessaloniki, Greece, has been a visiting professor at University College London, Department of Statistical Sciences, where the current book was written. He is well known in the area of Stochastic Mathematics and Applied Probability, in which he has published more than 50 papers in well-known journals. 2 2 j a t t r i l l @ w i l e y . c o m


• Introduces engineers to abstract concepts in mathematical stochastic processes and probability theory.

The volumes cover continuous time models by presenting the necessary material from continuous martingales, measure theory and stochastic differential equations as models for various assets, such as the Wiener process, Brownian motion, etc. It then builds, with many examples and intuitive explanations, the necessary stochastic analysis background i.e. its lemma, stochastic integration, Girsanovs theorem, etc. The book then guides the reader into the pricing of vanilla options in continuous time i.e. the continuous time models of Black and Scholes, followed by interest rate models and the models of Heath-JarrowMorton and the forward Libor model. The final part of the book presents the pricing of credit derivatives.

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Statistics – Operations Research

Business and Scientific Workflows: A Web ServiceOriented Approach

Advanced DynamicSystem Simulation: Model Replication and Monte Carlo Studies

Wei Tan, MengChu Zhou 978-1-118-17133-2 / 1-118-17133-0 256 pp. Pub: 29/04/13 Systems Engineering & Management This reference book for system engineers, architects, and managers focuses on how to design, analyze, and deploy Web service-based workflows for both business and scientific applications in a broad domain of healthcare and biomedicine. It discusses recent research and development results, as well as applications including healthcare and biomedical applications such as personalized healthcare processing, DNA sequence data processing, and electrocardiogram wave analysis. It also presents key methods such as Petri nets and social network analysis to advance the theory and applications of workflow design and Web service composition.

Granino A. Korn 978-1-118-39735-0 / 1-118-39735-5 280 pp. Pub: 15/04/13 Systems Engineering & Management This book focuses on DESIRE, a computer program for interactive modeling and simulation of dynamic systems, such as aerospace vehicles, control systems, or biological systems. It rivals books on MATLAB, boasting unique and cutting-edge simulation techniques using the DESIRE software.

• It presents updated research and development results on the composition technologies of Web services for eversophisticated service requirements from various users and communities.

• Written by the leading engineer who created DESIRE. • Instructs how to implement modern simulation software, DESIRE.

• The book presents some fundamental methods such as Petri nets and social network analysis to advance the theory and applications of workflow design and Web service composition.

• Includes two new chapters on Neural Networks. • Packaged with an open-source, personal-computer software CD, which contains 200 example programs that readers can edit and use in experiments.

• It presents practical and real applications of the developed theory and methods to such platforms as personalised healthcare and Biomedical Informatics Grid

• Also useful for applications in finance and environmental science. Computer scientists and engineers. Upper-level undergraduate students using DESIRE, as well as graduate level engineering and computer science students.

• Authors are both academic and major corporation(IBM) Systems Engineers, Project Managers, Enterprise Architects, Software developer in Enterprise Software. MengChu Zhou, Professor of Electrical and Computer Engineering and Director of Discrete-Event Systems Laboratory, New Jersey Institute of Technology (NJIT), Newark, also serves as Director of the MS in Power and Energy Systems Program and Area Coordinator of Intelligent Systems at NJIT. He has over 390 publications including 10 books, 180+ journal papers (majority in IEEE transactions), and 17 book-chapters. Wei Tan, Research Staff member, IBM TJ Watson Reseach Centre, has published one book chapter and over 20 peer reviewed journal and conference papers. He served in program committee for many international conferences and was the PC co-chair of the First IEEE/ACM Workshop on the application of Social Networking concepts to Cluster, Cloud, Grid and Services Computing (SN4CCGridS).

Granino Arthur Korn was Professor of Electrical Engineering, University of Arizona between 1957-1983, where he directed the Computer Engineering Research Laboratory. He took early retirement in 1983 to return to full-time software development and consulting. G.A. and T.M. Korn Industrial Consultants develop software and designs systems for interactive simulation of dynamic systems and neural networks. He is a member of Sigma Xi and EUROSIM, a Life Member of the Society for Computer Simulation, and a Fellow of the IEEE (1979).

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Product and Systems Development: A Value Approach

Autumn/Winter 2012 Mathematics Quantum Dynamics for Classical Systems: With Applications of the Number Operator

Stanley I. Weiss 978-1-118-33154-5 / 1-118-33154-0 250 pp. Pub: 24/06/13 Systems Engineering & Management A thorough treatment of system and product development in terms of value to all stakeholders, this unique book compiles over twenty years of research from a value perspective applied to vision and marketing to design, manufacturing, delivery, operations, and maintenance. •

Integrates the technical, productivity, and customer/end user elements in product and system development.

The result of over twenty years of research and practice at the Stanford Center for Professional Development.

Contains numerous illustrations, case studies, and online material.

F. Bagarello 978-1-118-37068-1 / 1-118-37068-6 218 pp. Pub: 14/01/13 Mathematics With a focus on the relationship between quantum mechanics and social science, this book introduces the main ideas of number operators while avoiding excessive technicalities that are not necessary to understand and learn the various mathematical applications. • Discusses the use of mathematical tools that are related to quantum mechanics and features applications in several contexts including finance, biology, and social science. • Features applications across diverse fields including stock markets and population migration and provides a unique quantum perspective on these classes of models. • Systematically shows how to use creation and annihilation operators for classical problems. • Addresses the recent increase in research and literature on the many applications of quantum tools in applied mathematics. • Ideal for applied mathematicians and physicists who need to expand their mathematical background to address problems from biology, psychology, economics, and the social sciences. • Written by a well-known physicist who clarifies numerous misunderstandings and misnomers while shedding light on new approaches in this growing area.

Graduate students in Aerospace, Mechanical, Civil, Electrical, Material engineering, and Management Science and Engineering. Practicing engineers, NASA, DoD, medical product developers. Stanley I. Weiss, PhD, has served as a Consulting Professor at Stanford University since 2000. He earned his PhD in Theoretical and Applied Mechanics at the University of Illinois, and is a graduate of the Advanced Management Program at Harvard Business School. Dr. Weiss developed much of his research in the fields of Product Design and Manufacturing at MIT and UC-Davis. He is a Past Chairman and current Advisory Board member at the Rensselaer Polytechnic Institute Departments of Mechanical, Aerospace and Nuclear Engineering.

Researchers, professionals, and academics in applied mathematics, economics, physics, biology, and sociology. Graduate-level and/or PhD-level book. Libraries. Fabio Bagarello, Professor, Department of Mathematical Methods & Models, University of Palermo, Italy, is author of approximately 100 journal articles,

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Public Key Cryptography: Applications and Attacks

Probability & Statistics Spatio-temporal Design: Advances in Efficient Data Acquisition

L. Batten 978-1-118-31712-9 / 1-118-31712-2 253 pp. Pub: 15/02/13 Cryptography

Werner Mueller, Jorge Mateu

This book gives a complete description of the current major public key cryptosystems, the underlying mathematics, and the most common techniques used in attacking them. This book is needed in order to provide a solid background to people who have jobs in government organizations, cloud service providers and large enterprises employing public key systems to secure their data.

978-0-470-97429-2 / 0-470-97429-X 384 pp. Pub: 30/01/13 Probability & Statistics A state-of-the-art presentation of optimum spatio-temporal sampling design - bridging classic ideas with modern statistical modeling concepts and the latest computational methods.

The first chapters of this text cover the theory of public key systems in current use, including ElGamal, RSA, Elliptic Curve and digital signature schemes. The underlying mathematics needed to build and study these schemes is provided as needed through the book. The latter half of the book examines attacks on these schemes via the mathematical problems on which they are based; these are the discrete logarithm problem and the difficulty of factoring integers. Each section of the book contains up to ten examples, including several which are computationally challenging and therefore need software support. The solutions to these examples are described in detail. In addition, each chapter contains forty to fifty problems for the reader and full solutions are provided in the appendix.

Provides an up-to-date account of how to collect spacetime data for monitoring, with a focus on statistical aspects and the latest computational methods.

Discusses basic methods and distinguishes between design and model-based approaches to collecting spacetime data.

Features model-based frequentist design for univariate and multivariate geostatistics, and second-phase spatial sampling.

Integrates common data examples and case studies throughout the book in order to demonstrate the different approaches and their integration.

Includes real data sets, data generating mechanisms and simulation scenarios. Accompanied by a supporting website featuring R code.

Explains fundamentals of Public Key Cryptography.

Illustrated with many examples and exercises.

Useful study tool for those taking the CISSP exam (Certified Information Systems Security Professional).

Solutions to the end-of-chapter problems are provided in Appendix.

Spatio-Temporal Design presents an excellent book for graduate level students as well as a valuable reference for researchers and practitioners in the fields of applied mathematics, engineering, and the environmental and health sciences.

University students at senior and masters levels, both in IT and in mathematics.

Jorge Mateu, Department of Mathematics of the University Jaume I of Castellon, Spain. Werner G. Müller, Department of Applied Statistics, Johannes Kepler University Linz, Austria.

Professor Lynn Batten holds the Deakin Chair in Mathematics and is the Director of the Information Security Research Group at Deakin University. As Associate Dean for Academic and Industrial Research at the University of Manitoba, her former institution, she established a number of agreements between the University and various industry and government sectors.

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Spatial and SpatioTemporal Geostatistical Modeling and Kriging

Willful Ignorance: The Blind Side of Statistics

G. Fernández-Avilé, José M. Montero, Jorge Mateu Mahiques

978-0-470-89044-8 / 0-470-89044-4 320 pp. Pub: 15/04/13 Probability & Statistics

Herbert I. Weisberg

978-1-118-41318-0 / 1-118-41318-0 400 pp. Pub: 15/04/13 Probability & Statistics

"... the quality of the proposal serves as a highly convincing evidence of the author's mastery of the subject." Necip Doganaksoy (General Electric Research).

Statistical Methods for Spatial and Spatio-Temporal Data Analysis provides a unified approach to modeling spatial and spatio-temporal data whilst combining formal statements of the results including mathematical proofs with informal and naïve statements of classical and new results. This book is divided into several parts, part I focuses on the classical approach and techniques to deal with data showing spatial dependencies, part II presents an up-to-date account of strategies for dealing with data evolving in space and time whilst part III enters a new area in Geostatistics when data come in form of functions.

In this book, the author explains how the tacit principle of "willful ignorance" has led to a deep and troubling divide between qualitative and quantitative modes of research that will increasingly constrain scientific progress, and can only be bridged by a broadened conception of statistical methodology.

Furthermore this book provides a detailed exposition of spatial kriging methodology illustrating the different situations that the researcher could face. An in-depth look into spatial dependencies is also featured, which explores valid candidate covariance functions and variograms for representing the existing spatial dependencies in the data, how to construct the empirical counterparts and the methods for selecting a valid covariance function or variogram from the empirical counterpart. Finally, the book presents a series of methods indicating the goodness of the predictions which are provided.

A clearly written introductory chapter lays out the author’s provocative thesis.

Numerous interesting examples, both hypothetical and real, illustrate and support the main premise.

A non-technical historical survey of core statistical concepts views current statistical thinking from a unique perspective.

Speculations about the possible future evolution of statistics are envisioned.

A bold but well-reasoned prescription for an expanded research framework grounded in causal (counterfactual) ideas is proposed.

Of particular interest to clinicians (both practicing and in research) who welcome the book's message of observational statistics; for biomedical and social science researchers and students, as well as business leaders and policy-makers; for professionals in statistics and related fields; as supplemental reading in general statistics and data analysis courses at both the undergraduate and graduate levels; for the "intellectually rich" who feel either disenfranchised from or intrigued by the field of statistics.

Gema Fernández-Avilés and José M. Montero, Faculty of Law and Social Sciences, University of Castilla-La Mancha, Toledo, Spain. Jorge Mateu Mahiques, Department of Mathematics, University Jaume I of Castellon, Spain.

Herbert L. Weisberg, is Founder and President of Correlation Research, Inc. of Needham, MA, a consulting firm that specializes in application of statistics and database technology to a variety of business problems. He has either worked for or consulted with a number of health- and education-related organisations in the Boston area. He has more than a hundred published articles or legal testimonies to his credit, including a bestselling book on bias reduction (Copyright 1980) and a new, more current book on the subject, both by Wiley. He has previously served as the President of the Boston Chapter of the American Statistical Association. 2 6 C o n t a c t :

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General Theory of An Introduction to Coherent Lower Previsions Imprecise Probabilities Matthias Troffaes, Gert de Cooman

Frank Coolen, Matthias Troffaes, Gert de Cooman

978-0-470-72377-7 / 0-470-72377-7 288 pp. Pub: 26/02/13 Probability & Statistics

978-0-470-97381-3 / 0-470-97381-1 416 pp. Pub: 11/03/13 Probability & Statistics

Extends the classic theory of lower previsions to deal with unbounded quantities, often found in optimisation problems.

A much needed introduction to the growing field of Imprecise Probability. In recent years, the theory has become widely accepted and has been further developed, but a detailed introduction is needed in order to make the material available and accessible to a wide audience. This will be the first book providing such an introduction, covering core theory and recent developments which can be applied to many application areas. All authors of individual chapters are leading researchers on the specific topics, assuring high quality and up-to-date contents.

Currently, the theory of lower previsions deals exclusively with bounded random quantities, making it difficult to apply in many instances. The first book to present an extension to the existing theory of lower previsions, General Theory of Coherent Lower Previsions builds on existing theory, bringing together very powerful theories before developing them further. The author lays the foundations for increased practical work in applying the theory to a growing number of statistics, mathematics, and engineering problems making it suitable for researchers, practitioners, and students in these fields. •

Illustrates ways in which the theory of Lower Previsions can be extended to cover a larger set of random quantities.

Highlights a crucial problem in the theory of imprecise probability and provides a detailed theory on how to resolve it.

Includes illustrative examples to understanding of the main concepts.


Chapter topics include: Sets of desirable gambles; Coherent lower (conditional) previsions; Special cases and links to literature; Decision making; Graphical models; Classification; Reliability and risk assessment; Statistical inference; Structural judgments; Aspects of implementation (including elicitation and computation); Models in finance; Game-theoretic probability; Stochastic processes (including Markov chains); Engineering applications.

A cutting edge theoretical approach to compliment the Wiley Series in Probability and Statistics.

Authored by the leading authorities in the field.

Provides a comprehensive introduction to imprecise probabilities, including theory and applications reflecting the current state-of-the-art.

Each chapter is written by leading experts in their field.

Is supported by a website featuring developed software for the implementation of the methods featured in the book.

Thomas Augustin, Department of Statistics, University of Munich, Germany. Frank Coolen, Department of Mathematical Sciences, Durham University, UK. Gert de Cooman, Research Professor in Uncertainty Modelling and Systems Science, Ghent University, Belgium. Matthias Troffaes, Department of Mathematical Sciences, Durham University, UK

Matthias Troffaes, Department of Mathematical Sciences, Durham University, UK, has published papers in a variety of journals, and written two book chapters. Gert de Cooman, SYSTeMS Research Group, Ghent University, Belgium has many years' research and teaching experience. He serves/has served on the Editorial Boards of many statistical journals, publishing over 40 journal articles, and is an editor of the Imprecise Probabilities Project. He has also written chapters for six books, and has co-edited four. S t a t i s t i c s

Essential reading for researchers in academia, research institutes and other organisations, as well as practitioners engaged in areas such as risk analysis and engineering.

Researchers and practitioners working in statistics, mathematics, engineering, artificial intelligence and decision theory. Researchers and students interested in (imprecise) probabilities and unbounded quantities.


The first book to chart the development and applications of this growing subject.


Lays the foundations for increased practical work in applying the theory to a growing number of statistics, mathematics and engineering problems.

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Computational and Statistical Methods for Protein Quantification by Mass Spectrometry

Visual Data Mining: The VisMiner Approach Russell Anderson 978-1-119-96754-5 / 1-119-96754-6 208 pp. Pub: 31/12/12 Probability & Statistics

Ingvar Eidhammer 978-1-119-96400-1 / 1-119-96400-8 320 pp. Pub: 18/03/13 Probability & Statistics

Data mining has been defined as the search for useful and previously unknown patterns in large datasets. Yet when faced with the task of mining a large dataset, it is not always obvious where to start and how to proceed. The purpose of this book is to introduce a methodology for data mining and to provide guidance to the application of that methodology using software specifically designed to support the methodology, presenting an overview of the methodology, followed by a sequence of exercises. The exercises use VisMiner which is a powerful visual data mining tool, designed around the methodology.

Mass spectrometry is a powerful analytical technique that is used to identify unknown compounds, quantify known materials, and elucidate their molecular structure and chemical composition. In mass spectrometry (MS) based protein quantification, computational tools are essentially required to process the large amounts of generated data. This book aims to systemize and describe the different approaches used for performing protein quantification by mass spectrometry, providing the understanding of computational and statistical methods used for analyzing the data from such experiments. Understanding this process is important for bioinformaticians developing software for protein quantification and allows biologists that use the software to become better equipped to plan their experiments and interpret the obtained results. Numerous examples are featured throughout the book as well as exercises and an accompanying solution manual, available online.

This book presents data mining tools, data visualisations and provides a comprehensive set of non-trivial datasets and problems with accompanying software that support students in learning, understanding, and practicing the entire data mining process. Visual Data Mining introduces the methodology for conducting data mining analysis along with data visualisation approaches to data mining using VisMiner. VisMiner is a data mining tool that has been developed specifically to bridge the gap between theory and practice.

Ingvar Eidhammer, Department of Informatics, University of Bergen, Norway. Harald Barsnes, Department of Biomedicine, University of Bergen, Norway. Geir Egil Eide, Centre for Clinical Research, Haukeland University, Norway. Lennart Martens, Department of Biochemistry, Faculty of Medicine and Health Sciences, Ghent University, Belgium.

Presents an overview of the methodology for conducting data mining analysis.

Introduces data visualisation approaches to data mining using VisMiner.

Explores classification modelers and regression modelers, data types and common data formats for VisMiner compatibility.

Includes datasets and examples for hands-on practice for users.

Provides a data mining step checklist supported by VisMiner tools.

Includes a free student license for VisMiner.

Graduate students and researchers involved in graphical modeling, applied statistics or business intelligence. Business intelligence analysts and statisticians, compliance and financial experts in both commercial and government organisations across all industry sectors. Russell K. Anderson, Information & Decision Management Department, West Texas A&M University, USA.

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The R Book, 2e

Improving Surveys with Process and Paradata

Michael J. Crawley

Frauke Kreuter

978-0-470-97392-9 / 0-470-97392-7 1008 pp. Pub: 30/01/13 Probability & Statistics

978-0-470-90541-8 / 0-470-90541-7 320 pp. Pub: 11/03/13 Probability & Statistics

The R language is recognised as one of the most powerful and flexible statistical software packages, and it enables the user to apply many statistical techniques that would be impossible without such software to help implement such large data sets. R is becoming essential both to carry out research and to understand it, as more and more people present their results in the context of R.

From a reviewer, "[This] is a hot topic. [It] is an area, which is still in its infancy, but it has ... reached the stage when the material can be codified into a book. The proposal is well suited to the series and Frauke Kreuter and the proposed team are very appropriate people to do it." The objective of the book is to provide readers with an overview of the best practices and cutting-edge research on the new topic of paradata in order to improve survey quality and total survey error.

This edition introduces the advantages of the R environment, in a user-friendly format, to beginners and intermediate users in a range of disciplines, from science and engineering to medicine and economics. The format enables it to be either read as a text, or dipped-into as a reference manual. The early chapters assume no background in statistics or computing, and introduce the reader to the basic concepts involved. In this way the reader is introduced to the assumptions that lie behind the tests, fostering a critical approach to statistical modeling. These early chapters have been thoroughly updated to take account of the way language has evolved since the publication of the first edition. Subsequent chapters examine more advanced topics, cementing what is learnt in the opening chapters, as well as benefiting more intermediate readers. Throughout the book, the reader's experience is furthered by practical guidance and the inclusion of numerous worked examples. • Introduces a clear structure/organisation with numbered section headings to help readers locate information more efficiently. • Revised to account for the evolution of R over the past five years. • Now includes links to other languages, such as C and Fortran, as well as R. • Supported by a website allowing examples from the text to be run by the user. • Features a new chapter on Bayesian Analysis.

Each chapter is written by a key expert in the field, both domestically and internationally.

The editor has taught several courses on paradata over the past two years. The material has been extensively class-tested and peer reviewed.

Case studies are discussed in an effort to draw attention to the challenges in automated data capturing and modeling of the complex structure of paradata.

Best practices are emphasised throughout.

PowerPoint slides and selected data sets will be available on an author-maintained web site.

The book is aimed at both producers and users of survey data. It can be used by researchers from academia, government, and the private sector. It will complement classes on data collection, survey methodology, and nonresponse and measurement error. It will have global appeal. Frauke Kreuter is Associate Professor in the Joint Program in Survey Methodology at the University of Maryland. She is Associate Editor of a number of key journals in her field of study. She is the author of Data Analysis Using Stata, Second Edition (2008). She has published over two dozen articles on the topic of paradata on which she is considered to be one of the world's foremost authorities.

Senior undergraduates, postgraduates and professionals in science, engineering and medicine. Senior undergraduates, postgraduates and professionals in economics, geography and the social sciences. Michael J. Crawley, Department of Biological Sciences, Imperial College of Science, Technology & Medicine is author of three bestselling Wiley statistics titles and five life science books. Previous edition – Licensed: Japanese

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Methods and Applications of Statistics in the Atmospheric and Earth Sciences

Nonparametric Predictive Inference Frank Coolen 978-0-470-72334-0 / 0-470-72334-3 256 pp. Pub: 07/08/13 Probability & Statistics

N. Balakrishnan 978-0-470-50344-7 / 0-470-50344-0 352 pp. Pub: 03/12/12 Probability & Statistics

This book will be the first on NPI and will provide an introduction to and overview of, the approach's current state of the art. It will be a self-contained treatment of the subject, introducing it to readers, and leading them on to a more advanced and specialist understanding. The author compares and contrasts NPI theory with classical statistical theory, pointing out the ways in which NPI can enhance current research in areas ranging from operations research to engineering and artificial intelligence.

Based on the multifaceted Encyclopedia of Statistical nd Sciences, 2 Edition, this concise book outlines the statistical concepts and applications that are essential for understanding modern research data gathered in the earth and atmospheric sciences. •

Demonstrates the purpose and application of statistical methods for conducting research on relevant and interesting issues that exist in today's environment, including global warming, pollution, droughts, and volcanic activity.

Contains newly-written contributions on topics such as nonlinear weather forecasting, ranked set sampling methodology, assessment of water pollution, and the application of spatial methods to geological studies.

Delves into quantitative methods, their application to research, and, where applicable, newly-discovered approaches to conduct research in fields such as meteorology, agriculture, geophysics, geology, and forestry.

Features relevant articles from the ESS-2e as well as newly-acquired contributions on topics from over 100 leading experts in academia and industry.

Provides a realistic alternative to the individual user who would like a quick reference containing encyclopedic information that pertains to their particular research interests and needs.

After the initial introductory chapter, the book provides a series of chapters outlining the use of NPI in specific settings, e.g. for real-valued random quantities or for multinomial data. This will be followed by chapters detailing further applications in statistics, providing examples such as NPI for statistical quality and process control, reliability and operations research, with a variety of examples such as maintenance and replacement problems, queuing situations and risk reliability inferences. The foundations and ideas behind NPI will be presented along with an examination and comparison of more traditional approaches of classical and Bayesian statistics, providing further insights into the advantages of NPI. Future directions and the accommodation of multivariate data will also be discussed. •

Provides an introduction and overview of the increasingly popular area of Nonparametric Predictive Inference (NPI).

Provides numerous interdisciplinary examples of NPI's applications, ranging from use for survival data to Bernoulli data.

Minimal background knowledge of basic mathematics and statistics is needed.

Students, academics, and researchers in the fields of geophysics, geology, geography, forestry, agriculture, and the related client disciplines who would like to expand their knowledge of statistical methods and applications in their area of practice.

Researchers and Academics in Statistics, Mathematics, Reliability and Operations Research. Advanced level students of Statistics, Mathematics, Reliability, OR and Artificial Intelligence.

N. Balakrishnan, Professor, Department of Mathematics & Statistics, McMaster University, Ontario, Canada. He is the author of over twenty Wiley books and is Co-Editor-inChief of the Wiley's Encyclopedia of Statistical Sciences, Second Edition (published in 2005). Dr. Balakrishnan's career spans over twenty years with academic and research exploration in the areas of statistical distributions, multivariate analysis, and industrial statistics.

Professor Coolen is the leading expert on NPI, and has been the driving force behind its development since the mid-1990s. He is an invited speaker on this topic globally, and serves on the Editorial Boards of 3 major statistical journals and has published over 60 papers in international journals, and authored and co-authored a variety of book chapters, including one in Wiley's forthcoming Encyclopedia of Statistics in Quality and Reliability. 3 0

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Categorical Data Analysis 3e

An Introduction to Social Network Analysis with Applications on Organizational Risk

Alan Agresti 978-0-470-46363-5 / 0-470-46363-5 768 pp. Pub: 19/11/12 Probability & Statistics

Ian McCulloh, Helen Armstrong, Anthony Johnson

"A ‘must-have' book for anyone expecting to do research and/or applications in categorical data analysis." Statistics in Medicine

978-1-118-16947-6 / 1-118-16947-6 250 pp. Pub: 08/04/13 Operations Research & Management Science

This classic book summarizes the latest and best methods for univariate and correlated multivariate categorical responses than any rival of its kind on the market today. •

All content has been meticulously updated to reflect recent developments of new methodology.

The author provides a unified generalized linear models approach that connects logistic regression and Poisson and negative binomial regression for discrete data with normal regression for continuous data.

A new chapter on Bayesian techniques has been added, reflecting the growing popularity in frequentist analyses.

There is now a stronger emphasis on the key topics of clustered data, robust variables, ordinal data, and interpretation, among several others.

More than 125 analyses of real data sets are presented in order to illustrate application of the methods.

Over 100 new exercises (now totaling 700) have been added throughout, again differentiating between application and theory/methods.

Software discussions have been expanded from SAS to include R subroutines.

Authored by military and intelligence professionals, this comprehensive book on the new and emerging topic of Social Network Analysis introduces network analysis and hones in on basic centrality measures, social links, subgroup analysis, data sources and more. • Includes practice problems and exercises. • Contains examples of calculations and formulas to illustrate mathematical calculations for social network measures. • Authored by professionals who have trained soldiers in Iraq and Afghanistan, local and national police, and other industry professionals on applications of social network analysis. • Covers content in an accessible way for both practitioners and students. Practitioners in management, intelligence and law enforcement who wish to learn and apply social network analysis to their respective fields. Also, those who teach workshops in social network analysis. Final year undergraduates and entry-level graduate students. Ian A. McCulloh Assistant Professor, Dept. of Behavioral Sciences & Leadership, West Point. MAJ (Major) McCulloh th has served on operational assignments with the 10 Mountain Division, Commander of Weapons of Mass st Destruction Detachment in the 1 Special Forces Group th (Airborne), and Commander of the 11 Chemical Company. Currently, he is working on modeling and detecting statistically significant changes in networks, and methods for unobtrusive geo-location and social network data collection for the Network Science Center. Helen Armstrong, Associate Professor, School of Information Systems, Curtin University of Technology, Perth, Australia, has more than 20 years’ experience in teaching and researching in ICT network security, analyses of networks and systems, information systems strategy and management and problem solving in business environments. Anthony Johnson, Professor, Dept. of Mathematics, US Military Academy, West Point, where he combines signal communications with his background in linear algebra to explore and exploit networks. Afghanistan.

Designed as a reference book for statisticians and biostatisticians as well as scientists and graduate students practicing statistics or as a serious one or two semester textbook at the upper undergraduate or beginning graduate level in analysis of multivariate and categorical data Alan Agresti, is Distinguished Professor in the Department of Statistics at the University of Florida. He has published extensively on categorical data methods and has presented courses on the topic for universities, companies, and professional organisations worldwide. A Fellow of the American Statistical Association, he is also the author of two other Wiley texts on categorical data analysis and coauthor of Statistical Methods for the Social Sciences. Previous editions – Licensed: Korean, Simplified Chinese An Introduction to Categorical Data Analysis all editions – Licensed: Japanese, Korean, Orthodox Chinese, Simplified Chinese. M a t h s


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Biostatistics and Clinical Trials

Statistical Modelling of ICU Data

Population-based Cancer Survival Analysis

Sylvie Chevret, Matthieu Resche-Rigon, Romain Pirracchio 978-1-119-97926-5 / 1-119-97926-9 288 pp. Pub: 03/18/13 Biostatistics and Clinical Trials

Paul Dickman, Timo Hakulinen 978-0-470-02859-9 / 0-470-02859-9 320 pp. Pub: 06/02/13 Biostatistics and Clinical Trials

The Intensive Care Unit (ICU) is one of the major components of the current health care system. The complex task of collecting and analysing data on performance measures are made easier when clinical information systems are available. Although several clinical information systems focus on important aspects as computerised physician order entry systems and individual patient tracking information, few have attempted to gather clinical information generating full reports that provide a panorama of the ICU performance and detailed data on several domains as mortality, length of stay, severity of illness, clinical scores, nosocomial infections, adverse events and adherence to good clinical practice.

There has been increased interest in studying cancer patient survival in recent years, which has prompted advances in methods for estimating and modeling cancer patient survival. This book is the first focused on this topic, and uses real data and software to illustrate the methods involved. The supporting website provides code to enable readers to reproduce the analysis top illustrate the examples included in the book. The book presents methods for population-based cancer survival analysis, that is, the analysis of patient survival using data collected by population-based cancer registries. The primary focus will be on the statistical methods but non-statistical issues that arise in population-based studies of cancer patient survival, such as registration, coding and classification, and follow up procedures are also discussed. • There are a number of books on clinical survival studies, however this will be the first to focus on the concept of relative survival analysis. • Based upon the authors' numerous successful courses in population-based cancer survival analysis. • Supporting website to include data and specially developed SAS and Stata code. • Presents methods that can be applied to the study of other diseases and to non-population based studies. • Part of the Statistics in Practice: Human & Biological Sciences Series.

This book presents the statistical approaches, with special focus on innovative approaches, that allow handling of the specificities of ICU data. The book covers the various clinical endpoints used in ICU, notably the different measures of mortality, of nosocomial events, of durations (lengths of stay, durations of support, etc.), as well as the modelling of severity scores or biological measures over time, ie, the analysis of repeated measures. Furthermore, the book will deal with data from randomised clinical trials, with some emphasis on the RCT that ought to be done in pandemic settings (such as H1N1), as well as data from observational nonrandomised studies, including registries that are often used in ICU or assessment of care that could not be easily randomly assigned (such as ICU care itself). The performances measures in ICU will be addresses and will look at the centre effect and the volume effect, since there are mostly used to compare ICUs.

Researchers (primarily epidemiologists and biostatisticians) employed at cancer registries or cancer epidemiology units. Epidemiologists and biostatisticians in general.

Practitioners, statisticians and clinicians involved in the analysis of ICU data. Graduate students and clinicians involved in the analysis of ICU data.

Paul Dickman, Associate Professor of Biostatistics, Department of Epidemiology, Karolinska Institutet, Stockholm, Sweden has published 17 papers in the field of cancer survival analysis alone. Timo Hakulinen, Finnish Cancer Registry, Helsinki, Finland, is the Director of the Finish Cancer Registry, and has been working in this field since 1974, publishing 91 papers within it. He has also coauthored chapters in two books. Between them, Drs. Dickman and Hakulinen have taught more than 20 courses on cancer survival analysis.

Sylvie Chevret, Matthieu Resche-Rigon and Romain Pirracchio, Department of Biostatistique et Informatique Médicale, Hôpital Saint-Louis. Paris, France.

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Understanding Large Temporal Networks and Spatial Networks: Exploration, Pattern Searching, Visualization and Network Evolution

Applied Missing Data in the Health Sciences Xiao-Hua (Andrew) Zhou, Leslie Taylor, Chuan Zhou 978-0-470-52381-0 / 0-470-52381-6 384 pp.

Biostatistics and Clinical Trials

Patrick Doreian, Vladimir Batagelj, Anuska Ferligoj, Natasa Kejzar

This book provides a modern, hands-on guide to the essential concepts and ideas for analyzing data with missing observations in the field of biostatistics.

978-0-470-71452-2 / 0-470-71452-2 416 pp. Pub: 06/02/13 Biostatistics and Clinical Trials

The most current and active areas of missing data research are discussed, including casual inferences for randomized trials with non-compliance, and missing data in diagnostic tests. Chapters are organised by types of data, allowing readers to work with the book according to their specific research or academic needs. Examples and data sets can be easily replicated using the SAS®, Stata®, R, and WinBUGS software packages. Rather than theory, emphasis is placed on hands-on application of various methods, including Bayesian, likelihood, and multiple imputations.

A comprehensive and over-reaching work by an excellent team of authors at the forefront of this hot topic. This book explores social mechanisms that drive network change and link them to computationally sound models of changing structure to detect patterns. This text identifies the social processes generating these networks and how networks have evolved. •

Authors are leaders in this evolving subject.

Provides an overview of different approaches to studying large temporal and spatial networks.

An existing popular website is available for data download Contains multiple examples of networks.

Examines evolutionary networks generative and network mechanisms.

Supported by downloadable datasets via website link.

Features bibliometric dynamics and the structure of science.

Xiao-Hua (Andrew) Zhou, Professor, Department of Biostatistics, University of Washington and Director and Research Career Scientist, Biostatistics Unit, Veterans Affairs Puget Sound Health Care System. Dr. Zhou is Associate Editor of Statistics in Medicine and has published over 150 journal articles in his areas of research interest. A Fellow in the American Statistical Association and the Royal Statistical Society, Dr. Zhou is the coauthor of Statistical Methods in Diagnostic Medicine (Wiley). Leslie Taylor, Mathematical Statistician, Services Research and Development, Veterans Affairs Puget Sound Health Care System. Chuan Zhou, Assistant Professor of Biostatistics, School of Medicine, Vanderbilt University.

Patrick Doreian, Professor Emeritus, University of Pittsburgh, has published over 100 articles in academic journals and book chapters. His co-authored book Generalized Blockmodeling received the Harrison White Outstanding Book Award in 2007. Anuska Ferligoj, Professor of Statistics, Faculty of Social Sciences, University of Ljubljana. Natasa Kejzar, Teaching Assistant of Informatics, University of Ljubljana, Faculty of Social Sciences. Vladimir Batagelj, Professor of Discrete and Computational Mathematics, University of Ljubljana and is chair of the Department of Theoretical Computer Science at IMFM, Ljubljana. He is a member of the editorial boards of Informatica and the Journal of Social Structure &

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The authors present case study examples in most chapters to illustrate the real-world use of the discussed methods.

As a text for courses on biostatistics at the upperundergraduate and graduate levels; as a resource for health science researchers and applied statisticians; and academic libraries.

Scientists: social networks, analysis, bibliometrics, cluster analysis, network visualisation, computer science and social sciences. Researchers in social network analysis and in its applications. Postgraduate students.

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Longitudinal Data Analysis, 2e

How to Design, Analyse and Report Cluster Randomised Trials in Medicine and HealthRelated Research

Donald Hedeker, Robert D. Gibbons 978-0-470-88918-3 / 0-470-88918-7 448 pp. Pub: 06/10/13 Biostatistics and Clinical Trials

Michael J. Campbell, Stephen J. Walters

This book presents and describes methods for analysis of longitudinal data, with a strong emphasis on the application of these methods to problems in the biomedical and behavioral sciences.

978-1-119-99202-8 / 1-119-99202-8 320 pp. Pub: 04/03/13 Biostatistics and Clinical Trials

This is an important book because longitudinal data are increasingly common in many areas of research, and methods of analysis of such data are not well understood by data analysts. Therefore, the book is geared more toward users, and not developers, of statistics.

Health technology assessment often requires evaluation of interventions implemented for clusters of individuals at the level of the health service organisation unit. This book delivers practical guidance on the design and analysis of cluster randomised trials (cRCTs) in health care research. It looks at conventional simple methods to tackle problems involved in cRCTs, including the t-test to compare aggregate cluster level summaries between groups, before addressing more advanced approaches using individual patient level data, including marginal and random effects generalised linear models, for continuous, binary, count and time to event outcomes. Clear guidance is given on how to present the results of such analyses and is illustrated throughout with real life case studies and worked examples from cRCTs, taken from the author’s experience of designing and analysing cRCTs.

Specific statistical procedures covered within the book include: repeated measures analysis of variance, multivariate analysis of variance for repeated measures, random-effects regression models (RRM), covariancestructure models, generalized-estimating equations (GEE) models, and generalizations of RRM and GE for categorical outcomes. This book emphasises methods for analysis of longitudinal data analysis, which are extensively illustrated using real examples. The authors have chosen not to focus on software in the book, though some syntax examples are provided. Many programs are available for the analyses presented in this book including SAS, SPSS, SYSTAT, HLM, MLwiN, MIXREG/MIXOR and Mplus.

Applied statisticians and quantitative researchers working in the biopharmaceutical industry, academia/higher education, medical and public health organisations. Post graduate working in biomedical statistics; public health/epidemiology, medical and health sciences students.

Researchers and professionals in medicine, public health, and pharmaceutical fields who desire coverage of modern statistical methods for analysing longitudinal data; graduate students.

Michael Campbell and Stephen Walters, School of Health & Related Research, University of Sheffield, UK.

Donald Hedeker, Professor of Biostatistics, Division of Epidemiology & Biostatistics, School of Public Health, University of Illinois, is author of over 140 journal articles and is a member of the American Statistical Association and the Biometric Society. He is also Associate Editor for the Journal of Educational and Behavioral Sciences. Robert D. Gibbons, PhD, is Director of the Center for Health Sciences and Professor of Biostatistics in the Division of Epidemiology and Biostatistics, School of Public Health, at the University of Illinois at Chicago. He has authored over 160 journal articles. Dr. Gibbons is a Fellow of the American Statistical Association and a member of the Institute of Medicine of the National Academy of Sciences.

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An Introduction to Adaptive Designs With Applications to Clinical Trials Using R

Statistics for Engineering A First Course in Probability and Markov Chains

Michael R. Chernick, Kenneth N. Anderson

Giuseppe Modica, Laura MPoggiolini 978-1-119-94487-4 / 1-119-94487-2 320 pp. Pub: 06/02/13 Statistics for Engineering

978-0-470-40445-4 / 0-470-40445-0 384 pp. Pub: 14/01/13 Biostatistics and Clinical Trials

Monte Carlo methods are numerical methods that use random numbers to compute quantities of interest. This is normally done by creating a random variable whose expected value is the desired quantity. One then simulates and tabulates the random variable and uses its sample mean and variance to construct probabilistic estimates.

This book presents an up-to-date, accessible, and authoritative look into the rapidly emerging study of statistical adaptive design. •

Practical examples are illustrated throughout the book.

Plentiful exercises are presented to further reinforce the concepts discussed.

Research in frontier studies and analyses is laid-out in such a way as to seem accessible and understandable.

The R language is employed throughout.

Bayesian influences are showcased when appropriate.

The emphasis is on "how to," not "why." Theory and concepts are presented first; then, followed by practical examples and applications.

Historical developments of the presented as sidebars to the theory.

An author-driven web site is available with selected answers to exercises, downloadable data sets, and hints for further reading.


Markov Chain Monte Carlo (MCMC) methods are a subset of Monte Carlo methods that are applicable to a very wide range of problems. They are used to sample from complicated multivariate distributions that are not computable in practice and from which direct sampling (Monte Carlo) is not feasible A First Course in Probability and Markov Chains presents an introduction to the basic elements in statistics and focuses in two main areas. The first part of the book looks at notions and structures in probability, including Combinatorics, probability measures, probability distributions, conditional probability, inclusion-exclusion formulas, random variables, dispersion indexes, independent random variables as well as Weak and Strong Laws of Large Numbers and Central Limit Theorem. A list of classical probability distributions, both discrete and continuous, is also included. In the second part of the book explores Discrete Time Discrete Markov Chains (DTDMC) which are discussed together with an introduction to Poisson processes and Continuous Time Discrete Markov Chains (CTDMC).


Adaptive procedures are of interest to investigators using statistical experimental designs, to those concerned with medical ethics, to people working in stochastic optimisation, control theory, decision theory, machine learning (computational learning theory), as well as to computer scientists. There should also be academic, institutional, and educational library/reference appeal.

The books main focus is in making use of measure theory notations that unify all the presentation, in particular avoiding the separate treatment of continuous and discrete distributions.

Michael R. Chernick, Senior Statistician, Auxilium Pharmaceuticals, Inc., has published two books with Wiley. He regularly attends conferences on adaptive designs where he presents cutting-edge research in the subject matter. He is a life member of American Statistical Association and a member in good standing of the Institute of Mathematical Statistics, the Biometrics Society and the International Society of Clinical Biostatisticians. Keaven Anderson, Executive Director, Clinical Biostatistics and Research Decision Sciences at Merck & Co., Inc. where he has personally designed many group sequential trials. M a t h s


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Under/graduate students in mathematics, science and engineering courses. Academics and students in mathematics, science and engineering courses. Giuseppe Modica and Laura Poggiolini, Dipartimento di Sistemi e Informatica, Università di Firenze, Italy.

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Statistics for Finance, Business and Economics

Financial Risk Modelling and Portfolio Optimization with R

Handbook of Financial Risk Management

Bernhard Pfaff 978-0-470-97870-2 / 0-470-97870-8 350 pp. Pub: 30/01/13 Statistics for Finance, Business & Economics

Ngai Hang Chan, Hoi Ying Wong 978-0-470-64715-8 / 0-470-64715-9 320 pp. Pub: 13/05/13 Statistics for Finance, Business & Economics

Presents advanced methods for modelling financial risks and portfolio optimisation using R. Introduces the latest techniques advocated for measuring financial market risk and portfolio optimisation, and provides a plethora of R code examples that enable the reader to replicate the results featured throughout the book.

This is the authoritative volume on risk management techniques and simulations as applied to financial engineering topics, theories, and statistical methodologies. The scope of the content is wider - and deeper - than any known competitor, including topics such as volatility, fixedincome derivatives, LIBOR Market Models, risk measures, and over two-dozen recognised simulation models. Throughout the material is organised around different asset classes. Although the primary focus is simulations, a complete algorithm may combine simulations with other techniques such as calibration, optimisation, and/or treebuilding. Readers see how these methods interact and complement each other.

Financial Risk Modelling and Portfolio Optimization with R: •

Demonstrates techniques in modelling financial risks and applying portfolio optimization techniques as well as recent advances in the field.

Introduces stylized facts, loss function and risk measures, conditional and unconditional modelling of risk; extreme value theory, generalized hyperbolic distribution, volatility modelling and concepts for capturing dependencies.

Extensive references to the literature are given for further study.

Explores portfolio risk concepts and optimisation with risk constraints.

Data sets, computer subroutines, and author commentaries on a chapter-by-chapter basis are posted on a dedicated web site.

Enables the reader to replicate the results in the book using R code.

Is accompanied by a supporting website featuring examples and case studies in R.

Introduces stylized facts, loss function and risk measures.

Presents advanced methods for modelling financial risks and portfolio optimisation using R.

Accompanied by a supporting website featuring examples and case studies in R.

As a handy, but complete reference for practitioners in the fields of finance, business, applied statistics, econometrics, and engineering and for libraries in academic and corporate settings; as a supplement to courses on financial risk management and simulation in similar departments and business schools at the graduate and MBA level. NgaiI Hang Chan is Professor of Statistics and Director of the Risk Management (RM) Science Program at The Chinese University of Hong Kong. He received his Ph.D. from the University of Maryland in 1985. He has published a book with Wiley on time series and their applications to finance. He is an Associate Editor for four journals and a referee for countless publications and organisations. HoiYing Wong is an Assistant Professor in the RM Science Program at The Chinese University of Hong Kong. He received his Ph.D. from The Hong Kong University of Science and Technology in 2001. His areas of interest include data analysis, statistical computing, RM, and stochastic calculus.

Practitioners in finance and portfolio optimization (inc. banking). Graduate and postgraduate students in finance, economics, risk management. Bernhard Eugen Heinrich Pfaff, Director, Invesco Asset Management Deutschland GmbH, Germany.

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Statistics for the Social Sciences

Research Methods for Postgraduates, 3e

Applied Time Series Analysis for the Social Sciences: Specification, Estimation, and Inference

Tony Greenfield 978-1-118-34146-9 / 1-118-34146-5 450 pp. Pub: 11/03/13 Statistics for the Social Sciences This new edition of Research Methods for Postgraduates is a response both to the success of the second edition and to the rapid change in methods and technology in this area. This book brings together guidance for postgraduate students on how to organise, plan, and conduct research from an interdisciplinary perspective. The wide-ranging coverage of this edition is enhanced by the addition of new chapters on social media, valuating the research process, Kansei engineering as well as looking at reporting medical research. Updates are provided on issues relevant to postgraduates in all subjects, from writing a proposal and securing research funds, to data analysis and the presentation of research, through to intellectual property protection and career opportunities. Like its predecessor, this edition is accessible and comprehensive, and is a must for any postgraduate student.

Regina Baker 978-0-470-74993-7 / 0-470-74993-8 448 pp. Pub: 25/03/13 Statistics for the Social Sciences Based on a successful ICPSR course, this book aims to present time series analysis to student and practitioners from a diverse set of backgrounds. The author assumes minimum mathematical background in order to provide an accessible and comprehensive approach to both the theory and practice of time series analysis. A wide range of topics are covered, including ARIMA probability models, Estimation and forecasting techniques, OLS and the Gauss-Markov assumptions, Intervention models and addresses newer methodologies such as GLS and ADL models, Vector Autoregression and Error correction models. It also introduces Pooled cross-section time series models and ARCH and GARCH models.

Postgraduates/research students in all subject areas of research methodology. Tony Greenfield is a visiting professor to the Industrial Statistics Research Unit (ISRU), the University of Newcastle-upon-Tyne and is past President of ENBIS, (European Network for Business and Industrial Statistics). He is a fellow of the Royal Statistical of the Royal Statistical Society and a Chartered Statistician. Tony received the William G Hunter Award presented by the Statistics Division of the American Society for Quality (ASQ).

The book is designed to break difficult concepts into manageable pieces whilst providing extensive case studies and exercises. It uses Lag operator algebra throughout the book to provide better understanding of applied time series analysis. Students and professional researchers following statistics and time series courses in the political sciences, public policy, sociology and economics. Regina M. Baker, Department of Political Science, University of Oregon, has extensive experience in teaching a time series course for The Inter-University Consortium for Political and Social Research (ICPSR) summer program.

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Maths & Statistics Spring-Summer 2013