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For more information on any of these titles please contact Julie Attrill jattrill@wiley.com


Maths & Statistics Rights Guide: Spring / Summer 2012 Probability & Statistics........................................................ 3 Introduction to Probability and Stochastic Processes with Applications/Blanco Castaned ......... 3 Modelling Under Risk and Uncertainty: An Introduction to Statistical, Phenomenological and Computational Methods/de Rocquigny .............................................................................................. 3 Classic Problems of Probability/Gorroochurn ................................................................................... 4 Bayesian Analysis of Stochastic Process Models/Insua ................................................................. 4 Statistical Learning/Huang ................................................................................................................. 5 The R Book 2e/Crawley ....................................................................................................................... 5 Statistical and Machine Learning Approaches for Network Analysis/Dehmer .............................. 6 Structural Equation Modeling with MPlus: Methods and Applications/Wang ............................... 6 Statistical Monitoring of Complex Multivariate Processes/Kruger.................................................. 7 Methods of Multivariate Analysis 3e/Rencher ................................................................................... 7 Willful Ignorance: The Blind Side of Statistics/Weisberg ................................................................. 8 Probability, Statistics, and Stochastic Processes 2e/Olofsson ....................................................... 8 Statistical Inference: A Short Course/Panik ...................................................................................... 9 Probability and Stochastic Processes/Florescu ............................................................................... 9 General Theory of Coherent Lower Previsions/Troffaes ................................................................ 10 Optimal Learning/Powell ................................................................................................................... 10 Introduction to Linear Regression Analysis, 5e/Montgomery ........................................................ 11 Regression Analysis by Example 5e/Chatterjee ............................................................................. 11 Applied Regression Modeling, 2e/Pardoe ....................................................................................... 12 Common Errors in Statistics (and How to Avoid Them) 4e/Good ................................................. 12 Statistical Disclosure Control/Hundepool ........................................................................................ 13 Causality: Statistical Perspectives and Applications/Berzuini ...................................................... 13

Mathematics ....................................................................... 14 A First Course in Applied Mathematics/Rebaza ............................................................................. 14 A Classical Introduction to Galois Theory/Newman ....................................................................... 14 Theory of Computation/Tourlakis ..................................................................................................... 15 Handbook of Real-World Applications of Modeling and Simulation/Sokolowski ......................... 15 Mathematical Modeling with Multidisciplinary Applications/Yang ............................................... 16 Bayesian Estimation and Tracking: A Practical Guide/Haug ........................................................ 16

Biostatistics & Clinical Trials ........................................... 17 Basic and Advanced Structural Equation Models for Medical and Behavioural Sciences/Lee . 17 Survival Analysis: Models and Applications/Liu ............................................................................ 17 Computing Effect Sizes for Meta-analysis/Borenstein .................................................................... 18 Evidence Synthesis for Decision Making in Healthcare/Sutton..................................................... 18 Bayesian Methods in Biostatistics/Lesaffre .................................................................................... 19 M a t h s & S t a t i s t i c s R i g h t s G u i d e

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Multiple Imputation and its Application /Carpenter ........................................................................ 19 Design and Analysis of Experiments in the Health Sciences/van Belle ....................................... 20 Statistical Methods in Healthcare/Faltin .......................................................................................... 20

Statistics for Finance, Business & Economics .............. 21 Financial Statistics and Mathematical Finance: Methods, Models and Applications/Steland .... 21 Handbook of Volatility Models and Their Applications/Bauwens.................................................. 21 Understanding Business Research/Weathington............................................................................ 22 Handbook of Exchange Rates/Sarno ............................................................................................... 22 A Modern Theory of Random Variation: With Applications in Stochastic Calculus, Financial Mathematics, and Feynman Integration/Muldowney....................................................................... 23 An Introduction to Analysis of Financial Data with R/Tsay ........................................................... 23 Statistical Methods in Customer Relationship Management/Kumar............................................. 24 Applied Stochastic Finance/Vassiliou .............................................................................................. 24

Statistics for Social Sciences........................................... 24 The Visualisation of Spatial Social Structure/Dorling ..................................................................... 24 Understanding and Applying Research Design/Abbott .................................................................. 25 Agent-Based Computational Sociology/Squazzoni ........................................................................ 25

Statistics for Engineering ................................................. 26 Industrial Statistics with Minitab/Grima........................................................................................... 26 Using the Weibull Distribution: Reliability, Modeling and Inference/McCool ............................... 26 Statistics for Scientists and Engineers/Chattamvelli ...................................................................... 27

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Modelling Under Risk and Uncertainty: An Introduction to Statistical, Phenomenological and Computational Methods

Probability & Statistics Introduction to Probability and Stochastic Processes with Applications Liliana Blanco Castaned, Viswanathan Arunachalam, S. Dharmaraja

Etienne de Rocquigny

978-1-118-29440-6 / 1-118-29440-8 640 pp. Pub: 10/08/12 Applied Probability & Statistics

978-0-470-69514-2 / 0-470-69514-5 192 pp. Pub: 11/05/12 Applied Probability & Statistics

Featuring intuitive and motivating discussions throughout, this book presents the basic concepts and methods of probability and introduces its multiple applications in diverse fields of study including mathematics, statistics, business, and engineering.

A highly relevant and extremely timely volume, bridging the gap between theory and practice. •

Addresses a concern of very high relevance and growing interest for large industries or environmentalists: risk and uncertainty in complex systems.

Includes examples, exercises, and problems at varying levels, all of which has been classroom tested numerous times at three universities.

Provides readers with the needed background in probability concepts with a focus on stochastic modeling, such as queueing theory, and financial mathematics.

Material is extremely timely and plugs a large reality gap between general principles and actual practice in a wide range of large-scale problems of great practical importance and interest.

Discusses key issue of differentiating or aggregating the dimensions of risk/aleatory and epistemic uncertainty.

Illustrated with one favourite pedagogical example developed all along the book to facilitate the reading and understanding.

Link to an Open Source software on risk and uncertainty modeling on a website will be given.

Features many examples derived from current real-world applications, illustrating that multiple phenomena occur in nature and can only be accurately modeled by means of probabilistic techniques. Contains advanced topics for further learning including Ito integrals, martingales, and sigma algebras, which can be covered based on instructor preference and student enrollment.

Researchers in applied statistics, scientific computing, reliability, advanced mechanics, physics or environmental science. Corporate R&D experts and senior engineers in reliability or scientific computing in nuclear, aerospace & transport, oil or chemical industries, and in the environmental protection area. Experts from institutional/regulatory bodies involved in environmental protection, natural risk, industrial security of safety. Consulting engineers in reliability, risk assessment, natural risk and environmental management.

As a textbook for course in probability for students majoring in statistics, engineering, operations research, computer science, physics, and mathematics; as a reference for researchers and professionals of any discipline who need to make decisions based on data; also appropriate for professionals who are interested in learning how to accomplish effective decision making from data; and academic libraries. With its wide coverage, the book is appropriate for courses such as Introduction to Probability, Introduction to Probability and Markov Chain Models, Introduction to Probability and Stochastic Processes, Probability and Random Processes, Probability and Financial Mathematics, and Introduction to Financial Mathematics. M a t h s & S t a t i s t i c s R i g h t s G u i d e

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Classic Problems of Probability

Bayesian Analysis of Stochastic Process Models

Prakash Gorroochurn

David Insua, Fabrizio Ruggeri, Mike Wiper

978-1-118-06325-5 / 1-118-06325-2 320 pp. Pub: 07/09/12 Applied Probability & Statistics

978-0-470-74453-6 / 0-470-74453-7 320 pp. Pub: 18/05/12 Bayesian Analysis

Providing insight into one of the most fascinating and unique subjects in statistics, this book examines classic problems of probability that have both contributed to the field and have been of historical significance, including Parrondo' Amazing Paradox, Laplace's Rule of Succession, and Jacob Bernoulli and His Golden Theorem.

This unique book explores statistical inference and prediction of stochastic process from a Bayesian perspective. This book provides analysis of stochastic processes from a Bayesian perspective with coverage of the main classes of stochastic processing, including modeling, computational, inference, prediction, decision-making and important applied models based on stochastic processes. In offers an introduction of MCMC and other statistical computing machinery that have pushed forward advances in Bayesian methodology. Addressing the growing interest for Bayesian analysis of more complex models, based on stochastic processes, this book aims to unite scattered information into one comprehensive and reliable volume.

Detailing the history of probability, this book examines the classic problems of probability that have shaped the field and emphasises problems that are counter-intuitive by nature. Classic Problems of Probability is rich in the hostory of probability while keeping the explanations and discussions as accessible as possible. Each of 35 presented problems end with a listing of the latest relevant publications on the topic, and the author provides detailed and rigorous mathematical proofs as needed. For example, in the discussion of the Buffon needle problem, readers will find much more than the conventional discussion found in other books on the topic. The author discusses alternative proofs by Barbier that lead to much more profound and general results. The choice of random variables for which a uniform distribution is possible is also presented, which then naturally leads to a discussion on invariance. Likewise, the discussion of Bertrand's chord paradox involves much more than stating there are three well-known possible solutions to the problems; rather, the athor discusses the implications of the indeterminacy of the problem as well as the contributions made by Poincare and Jaynes. This approach was used for all of the problems in the book. •

Provides a clear and concise mathematical treatment and accurate history of classic problems in probability in chronological order

Presents problems that are mostly elementary such as Gerolamo Cardano and Games of Chance, The Chevalier de Mere Problem, and D'Alembert Slips; however, the problems do increase in complexity throughout the book including Of Borel's and Monkeys, Gamow, Stern, and Elevators, and Simpson's Paradox

Features detailed history of the subject as well as concise solutions and alternative interpretations

Contains all the core topics typically found in an undergraduate course and will be a valuable resource for students and those interested in the history of probability

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A unique book on Bayesian analyses of stochastic process based models.

Covers the main classes of stochastic processing including modeling and computational aspects.

Features inference, prediction and decision making.

Each chapter includes case studies.

Researchers in stochastic processes/Bayesian analysis, practitioners of OR stochastic modeling. Advanced postgraduates interested in these fields. Undergraduates interested in Stochastic processes/Bayesian Analysis.

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Statistical Learning

The R Book, 2e

Su-Yun Huang, Henry Horng-Shing Lu

Michael J. Crawley

978-0-470-46743-5 / 0-470-46743-6 528 pp. Pub: 11/10/12 Computational & Graphical Statistics

978-0-470-97392-9 / 0-470-97392-7 960 pp. Pub: 10/08/12 Computational & Graphical Statistics

This book presents an easy-to-read guide to the state-of-the-art techniques that are essential for deconstructing and analysing complex data.

R is one of the most powerful and flexible statistical software packages, and is fast becoming the universal language in which to obtain and present results. This edition provides the definitive practical and up to date guide to understanding and operating this essential software, and is accessible to all readers.

This book provides an interactive understanding of the key areas of statistical learning: unsupervised learning, supervised learning, dimension reduction, and kernel methods.

Reproducible examples and exercises throughout the book promote the relationship between computing and statistical learning.

This is the first book of its kind to utilise examples in MATLAB, featuring an extensive appendix that provides the reader with the necessary MATLAB code for working with the book's examples.

Each chapter contains material that has been extensively classroom tested, ensuring a fluid presentation of both classic and modern methods.

The book's algorithms and codes are accessible via the book's related Web site.

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. 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.

As a text for courses on statistics, machine learning, data mining, and advanced multivariate analysis at the upper-undergraduate and graduate levels; As a reference for researchers and pracitioners in the fields of statistics, engineering, computer science, biology, and medicine.

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.

Fully revised and updated bibliography and reference sections.

First edition Licensed: Japanese Statistics - An Introduction Using R – Licensed: Greek, Japanese M a t h s & S t a t i s t i c s R i g h t s G u i d e

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Statistical and Machine Learning Approaches for Network Analysis

Structural Equation Modeling with MPlus: Methods and Applications

Matthias Dehmer, Subhash C. Basak

Jichuan Wang

978-0-470-19515-4 / 0-470-19515-0 352 pp. Pub: 12/10/12 Data Mining Statistics

978-1-119-97829-9 / 1-119-97829-7 320 pp. Pub: 17/08/12 Environmental Statistics & Environmetrics

This book uniquely focuses on graph mining and classification techniques, and it also introduces novel graph classes appropriate for countless applications across many disciplines.

Presents both basic and advanced topics on SEM models using Mplus.

This book provides a general framework for structurally analysing graphs by bringing together known and novel approaches on graph classes and graph measures for graph classification.

The proposed methods are applied to different real data sets to demonstrate their ability. By providing different approaches based on experimental data, it will be clear which method is mostly suitable for a given graph classification problem.

Each chapter provides the fundamental information needed to understand and apply the presented methods.

This book is written at both introductory and advanced levels. Both basic concepts and examples of Class Factor Analysis (CFA) and SEM models are explored along with some recently developed advanced methods in SEM, such as mixture modeling and model-based power analysis and sample size estimate for SEM. The author introduces latent class analysis (LCA), latent transitions analysis (LTA), latent growth mixture model (LGMM), and latent class factor analysis (LCFA). In addition, Satorra-Saris's method, Monte Carlo simulation, and MacCallum-Browne-Sugawara's method for power analysis and sample size estimate for SEM are also introduced and the results of the different methods are compared. Mplus has recently gained popularity with the SEM user community and although there are a number of SEM books in-print and in-development there are currently no titles that present SEM with Mplus.

This book explores novel graph classes and their relationships among each other and also presents current and classical methods to classify and analyse networks. In contract to classical mathematical approaches, computational aspects such as machining learning, data mining, and information theory techniques are strongly emphasised throughout the book.

As a supplemental text for graduate level, crossdisciplinary courses in applied discrete mathematics, bioinformatics, pattern recognition, and computer science; as a reference researchers and practitioners in the fields of applied discrete mathematics, machine learning, data mining, biostatistics, computational and systems biology, computational statistics, computational linguistics and computational neuroscience; and academic and professional libraries. .

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Focuses on the methods and practical aspects of SEM models using Mplus.

Looks at recent advances in SEM, such as multi-group models and latent growth models (LGM).

Compares and contrasts SEM models to help readers in selecting the necessary method.

Introduces basic SEM models which can be applied to both health science and the social science field.

Presents step-by-step instructions specification and estimation.

Supported by a supplementary website featuring Mplus syntax and program code.

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Researchers and practitioners using structural equation modeling in the health and social sciences. Graduate students following advanced statistics courses in the health, life and social sciences.

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Statistical Monitoring of Complex Multivariate Processes

Methods of Multivariate Analysis 3e

Uwe Kruger, Lei Xie

978-0-470-17896-6 / 0-470-17896-5 736 pp. Pub: 06/09/12 Multivariate Analysis

Alvin C. Rencher

978-0-470-02819-3 / 0-470-02819-X 416 pp. Pub: 17/08/12 Multivariate Analysis

This book is an "excellent introduction to standard topics in multivariate statistical analysis (Technometrics 2003)."

A comprehensive overview over developments in multivariate statistical process monitoring over the last 20 years with a focus on the application to industrial processes. The book summarises recent advances in statistical-based process monitoring of complex multivariate process systems. It includes a broad range of applications of multivariate statistical techniques into the area of mechanical, manufacturing and power engineering, whilst traditionally such techniques are predominantly developed and applied in the chemical industry. The content is aimed at presenting the theoretical foundation of multivariate statistical process control, outlining the drawbacks of this technology, describing improvements aimed at addressing such deficiencies and demonstrating the usefulness of these improvements using industrial examples that relate to applications in the chemical, mechanical, manufacturing and power industry as well as simple simulation studies. The text also includes tutorial questions and worked examples. •

Contains a detailed theoretical background of the component technology.

Brings together a large body of work to address the field's drawbacks, and develops methods for their improvement.

Details cross-disciplinary utilisation, exemplified by examples in chemical, mechanical, manufacturing and power engineering.

Includes real life industrial applications, outlining deficiencies in the methodology and how to address them.

Includes tutorial questions, calculations and worked examples, to enhance the learning aspect.

This new edition, now with a co-author, offers a complete and up-to-date examination of the field.

Previously tedious topics, such as multivariate regression and MANOVA-related techniques, have been streamlined to make way for newer, more timely content.

Each chapter contains exercises, with corresponding answers and hints in the appendix, providing readers the opportunity to test and extend their understanding of the subject.

Over half a dozen "Synthesis Projects" have been added in an appendix to further explore concepts previously discussed.

The new edition also presents several expanded topics in the areas of Kronecker product; prediction errors; maximum likelihood estimation; and selective key, but accessible proofs.

The book strikes a nice balance between meeting the needs of statistics majors and students or professionals in other fields. The discussion of each multivariate technique is straightforward and intentionally comprehensive.

The book provides SAS routines and data sets via an FTP site.

As a textbook for upper-undergraduate or beginning-graduate level students in statistics and/or applied (social) sciences; as a reference work for practicing statisticians and/or clinicians

Suitable for anyone working in process control who needs to apply statistical methods in their work. Will also make useful supplementary reading for senior undergraduates or taught graduate students of engineering and statistics. M a t h s & S t a t i s t i c s R i g h t s G u i d e

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Willful Ignorance: The Blind Side of Statistics

Probability, Statistics, and Stochastic Processes 2e

Herbert I. Weisberg

Peter Olofsson, Mikael Andersson

978-0-470-89044-8 / 0-470-89044-4 320 pp. Pub: 07/09/12 Popular Interest Statistics

978-0-470-88974-9 / 0-470-88974-8 568 pp. Pub: 15/06/12 Probability & Mathematical 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)

This book is ideal for learning the interrelationships between probability, statistics, and stochastic processes and better prepares readers to collect, analyse, and characterise data in their chosen fields.

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. •

Praise for the First Edition: "This is an excellent textbook that covers the three subjects of its title at an undergraduate upper level in one single volume...well organised and neatly written..." (Mathematical Reviews)

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

Features expanded and revised chapter coverage on simulation, statistical inference, and Bayesian statistics with a focus on applications

Includes many new examples and problems, mostly at the elementary level. (The First Edition contained problems that were mostly moderately difficult to quite challenging.)

Provides new coverage on analysis of variance (ANOVA), basic asymptotic theory for maximum likelihood estimators, empirical distribution functions, general linear methods, multiple comparisons, Markov chain Monte Carlo (MCMC), renewal theory, Brownian motion, and martingales, among others

Presents the development of the basic concepts and results of probability theory and details statistical inference by including sections on linear regression, Bayesian statistics, and nonparametric methods

Uniquely covers probability, statistics, and stochastic processes while other related book either only treat probability theory and statistical inference or probability theory and stochastic processes

Extensively classroom tested in courses taught worldwide and reflects new and expanded coverage based on past user feedback

As a reference and resource for scientists and engineers in the fields of statistics, mathematics, industrial management, and engineering; as a course book in probability and statistics at the upper-undergraduate level; and academic libraries.

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Statistical Inference: A Short Course

Probability and Stochastic Processes

M. Panik

Ionut Florescu

978-1-118-22940-8 / 1-118-22940-1 440 pp. Pub: 10/08/12 Probability & Mathematical Statistics

978-0-470-62455-5 / 0-470-62455-8 512 pp. Pub: 31/08/12 Probability & Mathematical Statistics

This book features the essentials of basic statistics including statistical concepts, measures, and procedures, provides analytical integrity without being overly mathematical, and presents both descriptive and inferential techniques that are applicable to a broad audience.

Emphasising key theoretical concepts while also incorporating real-world applciations, this book serves as a balanced and rigoroum, yet accessible, presentation of probability and stochastic processes.

Features a condensed and straight forward presentation of the essentials of basic statistics and ensures proper understanding of concepts such as research hypothesis, statistical significance, randomness, central tendency, variability, reliability, cause and effect, etc., which are of paramount importance in future professional environments Offers sufficient material for a one-semester course and also provides additional topical coverage, i.e. Bayesian probability, two population means and proportions, bivariate regression and correlation, and statistical tests of independence and association, depending on instructor preference and reader interests and needs

This book takes a sophisticated approach to applied probability and stochastic processes, introducting the topic with a unique balance of theory and applications in a pedagogical, andaccessible format.

The presented material has been classroom tested by the author at both Purdue University and Stevens Institute of Technology, ensuring that the content and style are motivating and comprehensive.

Rather than overwhelm readers with an excess of abstract concepts, the book focuses on key theory only while also emphasising the importance of measure theory in understanding basic probability concepts.

Topics of coverage are mathematically rigorous but also incorporate traditional applied concepts so that readers can gain an intuitive sense of theory and its practice.

Conducts tests of the assumptions of randomness and normality and offers nonparametric methods when parametric approaches might not work

Includes precise and complete definitions of key concepts such as confidence limits, p-values, and the motivation for hypothesis testing, and also features extended discussion of randomness and causality

Interesting real-world applications and novel examples from the fields of business, mathematical finance, and engineering shed light on the topic's relevance in modern research.

Provides numerous example problems in each chapter, and detailed solutions are included in an appendix

Presents answers to numerous questions pertaining to statistical results, including "How are probabilities determined?", "Is probability the same thing as odds?", "What is the strength of the relationship between two variables?", and "What level of reliability is associated with any such estimate?"

Exercises throughout the book are accompanied by partial solutions, allowing readers to test their comprehension of the presented material.

As a text for upper-undergraduate and graduate level courses on probability and stochastic processes in mathematics, business, and electrical engineering departments; as a reference for researchers and pracitioners working in the areas of mathematics, engineering, and mathematical finance; and academic libraries.

As a course book for a one-semester course in probability, mathematical statistics, and/or statistical inference for upper-undergraduate and graduate students majoring in statistics, the natural sciences, the social sciences, and business; as a resource for practitioners who desire insights into statistical tools; and academic libraries. Prerequisites include high school algebra. . M a t h s & S t a t i s t i c s R i g h t s G u i d e

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General Theory of Optimal Learning Coherent Lower Previsions Warren B. Powell, Ilya O. Ryzhov Matthias Troffaes, Gert de Cooman

978-0-470-59669-2 / 0-470-59669-4 414 pp. Pub: 11/05/12 Probability & Mathematical Statistics

978-0-470-72377-7 / 0-470-72377-7 288 pp. Pub: 10/08/12 Probability & Mathematical Statistics

This book addresses the problem of efficiently collecting information from which to make decisions while maintaining a level of mathematical rigor that is appropriate for professionals and students alike from a variety of backgrounds including operations research, management science, and engineering management.

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

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

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

Authored by the leading authorities in the field.

Utilises a relatively new class of algorithmic strategies known as approximate dynamic programming, which merges dynamic programming (Markov decision processes), math programming (linear, nonlinear, and integer), simulation, and statistics

Originated as lecture notes for a cutting-edge course on Optimal Learning at Princeton University

Features mathematical techniques that are applicable to a variety of situations, from identifying promising drug candidates to figuring out the best evacuation plan in the event of a natural disaster

Introduces problems where the probability distributions are unknown, but where the opportunity to collect new information to improve estimates of parameters is present

Provides a series of mathematical algorithms that optimise decision-making in the face of uncertainty, covers important dimensions of a learning problem, and introduces a range of policies for collecting information

As a textbook for courses in optimal learning and statistical learning theory at the graduate and PhD level; as a reference on the subject matter for practicing operations researchers, engineers, applied mathematicians, statisticians, and computer scientists; and academic and corporate libraries.

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

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Presents optimal learning techniques with applications in energy, homeland security, health, sports, transportation science, biomedical research, biosurveillance, stochastic optimisation, high technology, and complex resource allocation problems

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Introduction to Linear Regression Analysis, 5e

Regression Analysis by Example 5e

Douglas C. Montgomery, Elizabeth A. Peck, G. Geoffrey Vining

Samprit Chatterjee, Ali S. Hadi 978-0-470-90584-5 / 0-470-90584-0 416 pp. Pub: 11/10/12 Regression Analysis

978-0-470-54281-1 / 0-470-54281-0 712 pp. Pub: 18/05/12 Regression Analysis

... It is an excellent source of information and example analyses concerning regression modeling for the beginning to moderately trained data analyst." (Journal of the American Statistical Association, March 2009)

Praise for the Fourth Edition: "This book is written by the best in the field and I strongly recommend it both as a textbook and as a handy resource manual for researchers and applied practitioners." - Technometrics

This book simply, clearly, and succinctly presents the essentials of regression analysis through practical applications.

"As with previous editions, the authors have produced a leading textbook on regression." - Journal of the American Statistical Association

This new edition features the following enhancements:

Clearly balancing theory with applications, this book describes both the conventional and less common uses of linear regression in the practical context of today's mathematical and scientific research. Beginning with a general introduction to regression modeling, including typical applications, the book then outlines a host of technical tools that form the linear regression analytical arsenal, including: basic inference procedures and introductory aspects of model adequacy checking; how transformations and weighted least squares can be used to resolve problems of model inadequacy; how to deal with influential observations; and polynomial regression models and their variations. The book also includes material on regression models with autocorrelated errors, bootstrapping regression estimates, classification and regression trees, and regression model validation. •

This Fifth Edition introduces and features the use of R and JMP software. SAS, S-Plus, and Minitab continue to be employed in this new edition, and the output from all of these packages can be found throughout the book.

Two new chapters have been added on Regression for Time Series Data and Experimental Designs for Regression Models.

Written by academics and field practitioners, this book presents a comprehensive and thoroughly up-to-date look at regression analysis, the most widely used technique in statistics today.

This new edition continues to be oriented toward the analyst who uses computers for problem solution.

The Appendix includes ample background material on the theory of linear models underlying regression analysis.

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A new Chapter 9, "Detection and Correction of Multicollineraity," which streamlines the old chapters 9 and 10 into one chapter for improved consistency and smoother continuity.

Updated data sets, in general, and new data sets in engineering, medical and health science, and business and economics.

Additional topics such as surrogate ridge regression, fitting nonlinear models, errors in variables, and ANOVA for designed experiments.

Reorganised, expanded, and upgraded exercises at the ends of each chapter.

A fully integrated Web page that provides data sets in R, SPSS, SAS, and Minitab.

Numerous graphical displays, which highlight the significance of visual appeal.

A central glossary of terms and formulae at the end of the book.

As a one-semester book for an upper-undergraduate or beginning graduate course in applied regression within departments of statistics, biostatistics, and social sciences; as a reference for readers who need a quick update on the methods and applications of regression; for purchase in academic and university library settings Previous editions licensed: Korean

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Applied Regression Modeling, 2e

Common Errors in Statistics (and How to Avoid Them) 4e

Iain Pardoe

Phillip I. Good

978-1-118-09728-1 / 1-118-09728-9 352 pp. Pub: 31/08/12 Regression Analysis

978-1-118-29439-0 / 1-118-29439-4 320 pp. Pub: 07/09/12 Statistics - Text & Reference

This new edition is a concise, applied treatment of statistical regression analysis for students and professionals with little or no background in calculus.

"[The author's] advice is usually wise, and always worth considering. Recommended as stimulating reading for the statistical sophisticate." - statistics.com

This book offers a practical, concise introduction to regression analysis for upper-level undergraduate students of diverse disciplines including, but not limited to statistics, the social and behavioral sciences, MBA, and vocational studies. The book's overall approach is strongly based on an abundant use of illustrations, examples, case studies, and graphics. It emphasises major statistical software packages, including SPSS®, Minitab®, SAS®, R, and R/S-PLUS®. Detailed instructions for use of these packages, as well as for Microsoft Office Excel®, are provided on a specially prepared and maintained author web site. Select software output appears throughout the text. To help readers understand, analyse, and interpret data and make informed decisions in uncertain settings, many of the examples and problems use real-life situations and settings. The book introduces modeling extensions that illustrate more advanced regression techniques, including logistic regression, Poisson regression, discrete choice models, multilevel models, Bayesian modeling, and time series and forecasting. New to this edition are more exercises, simplification of tedious topics (such as checking regression assumptions and model building), elimination of repetition, and inclusion of additional topics (such as variable selection methods, further regression diagnostic tests, and autocorrelation tests). •

This tried-and-true book presents a mathematically rigorous yet readily accessible foundation for statistical procedures.

A generous selection of problems--many requiring computer work--in each chapter with fully worked-out, selected solutions and more than a dozen case studies that elucidate the concepts and the applicability of the material;

Focuses on debunking popular myths, analysing common mistakes, and instructing readers on how to choose the appropriate statistical technique to address their specific task (includes a handy checklist that summarises necessary steps)

Contains 17 new sections throughout the book on topics such as epidemiological studies; distribution of data; baseline data incorporation; case control studies; correlation; simulations; statistical theory publication; cost-benefit analysis; biplots; effective use of color in graphics; linear regression versus linear behavior; instrumental variables; ecological regression; survival analysis; binomial outcomes, etc.

Offers revamped exercises and emphasises solutions as much as problems

Provides numerous new figures throughout as well as substantial changes in the writing transitions from chapter to chapter

Teaches readers how to decide between different methods and how to avoid making common errors that skew results

A chapter on modeling extensions illustrating more advanced regression techniques through the use of real-life examples and covering topics not normally seen in a textbook of this nature ;

As a reference for recent graduates entering the fields of research, industry, medicine, or government; as a supplementary book for a first- or second-semester statistics course; and libraries.

A nontechnical requirement of pre-calculus.

Previous editions Licensed: Russian

Fully prepares professionals and students to apply statistical methods in their everyday decision-making, using primarily regression analysis and modeling. It can be used as an upper undergraduate or beginning graduate level text in statistical regression or as a resource for professionals who have been away from the topic for a while. S p r i n g / S u m m e r

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Statistical Disclosure Control

Causality: Statistical Perspectives and Applications

Dr Anco Hundepool

Carlo Berzuini, Philip Dawid, Luisa Bernardinell

978-1-119-97815-2 / 1-119-97815-7 320 pp. Pub: 24/08/12 Survey Research Methods & Sampling

978-0-470-66556-5 / 0-470-66556-4 384 pp. Pub: 04/05/12 Time Series

This handbook provides technical guidance on statistical disclosure control and on how to approach the problem of balancing the need to provide users with statistical outputs and the need to protect the confidentiality of respondents.

A state of the art volume on statistical causality.

Statistical disclosure control is combined with other tools such as administrative, legal and IT in order to define a proper data dissemination strategy based on a risk management approach.

This book looks at a broad collection of contributions from experts in their fields. Providing a thorough treatment on statistical causality. Methods and their applications are provided with theoretical background and emphasis is given to practice rather than theory, with technical content kept to a minimum. Step-by-step instructions for using the methods are presented with a broad range of examples, including medicine, biology, economics, sociology and political science.

The key concepts of statistical disclosure control are presented, along with the methodology and software that can be used to apply various methods of statistical disclosure control. Examples will also be used to illustrate methods described in the book. The handbook is based upon material prepared by the leading National Institute of Statistics in Europe. The context is relevant globally, not just within the EU.

Presents a non-technical account of the major current languages, concepts and models in statistical causality.

Examples used throughout from medicine, biology, economics to political science aid the readers understanding.

Introduces all of the key concepts and definitions involved with statistical disclosure control.

Presents a high level overview of how to approach problems associated with confidentiality.

Discusses ways to implement causal inference tools using R, STATA and SAS.

Provides a broad-ranging review of the methods available to control disclosure.

Authored by renowned global experts including Sir David Cox.

Explains the subtleties of group disclosure control.

Examples feature throughout the book along with case studies demonstrating how particular methods are used.

Discusses microdata, magnitude tabular data, and remote access issues.

Written by experts within leading National Statistical Institutes.

Postgraduates, professional statisticians and researchers in academia and industry. Students and researchers

Practitioners and employees of national statistical offices and survey organisations who need to be informed and make decisions on disclosure limitation. Graduate or advanced undergraduate student of applied statistics and survey methodology.

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A Classical Introduction to Galois Theory

Mathematics A First Course in Applied Mathematics

Stephen C. Newman 978-1-118-09139-5 / 1-118-09139-6 296 pp. Pub: 17/08/12 Applied Mathematics

Jorge Rebaza 978-1-118-22962-0 / 1-118-22962-2 454 pp. Pub: 27/04/12 Applied Mathematics

With a focus on one central theme (the Impossibility Theorem) throughout, this highly accessible introduction to Galois theory presents a classical treatment of the topic and presents questions related to the solvability of polynomial equations by radicals. Modern points of view are also discussed in contrast to the historical development and context.

This book presents the needed mathematical theory as well as the proofs of the main theorems, and current real world applications are featured including Google's web page ranking algorithm, image compression, cryptography, chaos, and waste management systems.

Presents three versions of the Impossibility Theorem (the general polynomial of degree 5 or greater is not solvable by radicals): the first relies entirely on polynomials and fields, the second incorporates a limited amount of group theory, and the third takes full advantage of modern Galois theory

Blends historical material and classic ideas and methods to provide motivation for understanding the modern content and applications, which are found in later chapters and are complemented by extensive worked examples. The transition from classical to modern is facilitated by uniformity of notation and consistency of themes, including primitive elements, minimal polynomials, the Impossibility Theorem, and stabiliser groups instances.

Ideal for anyone who would like to develop a deeper appreciation of the origins of Galois theory, understand how some of the fundamental concepts emerged, and apply this knowledge to better understand more modern expositions.

Illustrates the power of Galois theory as a computational tool and examines a number of problems arising in the area of classical mathematics within the context of solvability of polynomial equations by radicals.

Presents exercises at the end of each chapter as well as appendices on group theory, finite fields, and number theory.

• Features both theoretical and application-oriented problems and exercises at varying levels of difficulty and select answers can be found in an appendix •

Written at a level that is accessible to practitioners and students in a wide range of scientific and engineering fields

Blends standard topics with modern areas of application and provides the needed foundation for transitioning to more advanced topics

Presented in a logical order and and flows smootly from one topic to the next

Utilises MATLAB to illustrate the theory and examples; however, previous knowledge of MATLAB or programming is not required

Designed for a one-semester course and incudes the needed background and essential tools under one cover

As a textbook for undergraduate courses in applied mathematics for students majoring in mathematics, engineering, and the natural sciences; as a reference and/or self-study guide for engineers and scientists working in government and industry; and academic libraries.

As a resource and reference for researchers and professional algebraists who are seeking an accessible beginner-level introduction to the topic; as an undergraduate and graduate-level course book or supplement for a second course in abstract algebra or a one semester course in Galois theory; as a supplement for a reading course or senior honors project on Galois theory; and academic libraries.

Jorge Rebaza, PhD, is Associate Professor in the Department of Mathematics at Missouri State University. The author of numerous journal articles, he is a member of the American Mathematical Society and the Society for Industrial and Applied Mathematics. Dr. Rebaza received his PhD in Applied Mathematics with a minor in Computer Science from Georgia Institute of Technology in 2002.

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Handbook of Real-World Applications of Modeling and Simulation

Theory of Computation George Tourlakis 978-1-118-01478-3 / 1-118-01478-2 416 pp. Pub: 29/06/12 Discrete Mathematics

John A. Sokolowski, Catherine M. Banks 978-1-118-11777-4 / 1-118-11777-8 352 pp. Pub: 11/05/12 Mathematical Modeling

With a focus on Unbound Register Machines, this book introduces new ideas and topics using real computer-related examples to help readers gain the skills and intuition that is key to successful programming. •

Details the theory of computation and explains the development of programs and large scale software that are well documented, correct, efficient, reliable, and easily maintainable

Presents an introductory approach and specifically omits specialised combinatorial topics for reader ease

Develops the theoretical limitations of computing in its various models of computation, from the most general model-URMs (Unbounded Register Machines)-down to the finite automation

Provides numerous programming examples including URMs, Loop Programs, FA (Deterministic Finite Automata) and NFA (Nondeterministic Finite Automata), and PDA (Pushdown Automata)

Focuses on the metatheory of "everyday computing" and the study of the theoretical limitations (uncomputability) of such computing in a technology-independent manner

Includes discussion of the portion of automata theory that deals with the regular and context free languages as well as the machines that recognise them

This handbook provides an introduction to various modeling and simulation methods and paradigms that are used to explain and solve the predominant challenges facing society, and six leading applications are featured, including transportation, homeland security, operations research, medicine, military science, and business process modeling.

As an upper-undergraduate course book for a first course in computer theory for students majoring in computer science, mathematics, and engineering; as a refresher and resource for computing professionals in areas including databases, computer architecture, language design, software engineering, and operating systems; and academic libraries.

Provides a practical one-stop reference on modeling and simulation and contains an accessible introduction to key concepts and techniques in self-contained chapters

Introduces, trains, and prepares readers from all disciplines, such as statistics, mathematics, engineering, computer science, economics, business, to use modeling and simulation in their studies and research

Emphasises a multidisciplinary approach by featuring chapters written by well-known experts and key researchers in the various fields related to modeling and simulation

Contains a collection of original ideas on modeling and simulation to help academics and practitioners develop a multifunctional perspective on these issues

Integrates modeling and simulation theories, methods, and data to analyse challenges that involve technological and social issues

Features case studies that are representative of fundamental areas of multidisciplinary studies and provides a concise look at the key concepts that make up the field of modeling and simulation

As a reference and/or refresher for academics and practitioners in operations research, business, management science, engineering, statistics, and mathematics, and computer science; as a textbook for graduate-level courses in modeling and simulation in engineering and computer science schools; and academic and corporate libraries. This handbook is designed for modeling and simulation professionals and students who are at a masters level proficiency in engineering.

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Mathematical Modeling with Multidisciplinary Applications

Bayesian Estimation and Tracking: A Practical Guide

Xin-She Yang

978-0-470-62170-7 / 0-470-62170-2 448 pp. Pub: 03/08/12 Applied Mathematics in Engineering

Anton J. Haug

978-1-118-29441-3 / 1-118-29441-6 448 pp. Pub: 12/10/12 Mathematical Modeling

This book provides a unified approach to Bayesian estimation with complete derivations of all tracking algorithms and successfully details numerical methods for evaluating density-weighted integrals for both Gaussian and non-Gaussian densities.

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 nternation markets. •

Features state-of-the-art mathematical modelling coverage and techniques in a variety of fields including finance, industry, theoretical and applied mathematics, engineering and machine learning, physics, chemistry, ecology, social science, and finance and economics 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.

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.

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

Focuses on new techniques and applications and presents balanced coverage of PDEs, discrete models, statistics, fractional calculus, numerical methods, applications, and case studies.

As a course book for upper-undergraduate and graduate courses in mathematical modeling, applied mathematics, modeling and simulation, numerical methods, economics and finance, operations research and optimisation, and computational engineering and is appropriate for students majoring in mathematics, computer science, physics, economics and business, engineering, and chemistry; as a refresher and resource for professionals.

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Presents a Bayesian approach to linear and nonlinear Kalman filters that flows naturally into the development of all variations of the particle filter for non-Gaussian cases

Provides very practical flow diagrams that quantify all estimation/tracking algorithms, each of which is supplemented with MATLAB® code

Thoroughly classroom-tested for the past few years and incorporates feedback from both students and professors of target tracking and filtering

Presents a conceptually cohesive roadmap that begins with fundamental principles and leads directly to derivations of many of the Gaussian estimation methods currently in use within a Bayesian framework

Contains a graphic block diagram that can be used as a tool in developing a software-coded tracking toolbox for each presented estimation method

Discusses the steps required to develop simulations for several very specific real-world problems as an aid in understanding the presented methods

As textbook for a one semester graduate course on estimation and tracking methods; as a reference for professionals (mathematicians and engineers) requiring a deeper understanding of the topics; and academic libraries. Prerequisites include a graduate level understanding of probability theory as well as familiarity with matrix linear algebra and numerical methods including finite differences as well as a working knowledge of Matlab.

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Biostatistics & Clinical Trials

Survival Analysis: Models and Applications

Basic and Advanced Structural Equation Models for Medical and Behavioural Sciences

Xian Liu 978-0-470-97715-6 / 0-470-97715-9 472 pp. Pub: 06/07/12 Survival Analysis Provides a practical guide to classic survival models, presenting newly developed techniques with real-world examples

Sik-Yum Lee, Professor Xin-Yuan Song 978-0-470-66952-5 / 0-470-66952-7 480 pp. Pub: 10/08/12 Models Bridges the gap between an introductory text and advanced level treatment, outlining SEM theory and its applications to real problems in the medical and behavioural sciences •

Addresses a topic of growing importance in an accessible style.

Provides a detailed explanation on how to apply Structural Equation Models (SEMs) in the medical and behavioural sciences.

Adopts a Bayesian Approach throughout.

Illustrates the methodology through simulation studies and examples with real data from business management to public health.

Demonstrates the application of the freely available software WinBUGS via a supplementary website featuring computer code and data sets.

Introduces both classic survival models and newly developed techniques with real-world examples

Presents basic theories and methods regarding survival analysis

Discusses how to perform analysis of survival data by following examples with SAS code

Looks at both classic and advanced survival models

Accessible for those with a minimum mathematical background

Applied statisticians and quantitative methodologists involved in the medical, biological and behavioral sciences. Graduate students involved in survival analysis.

Researchers and advanced level students in areas such as statistics, biostatistics, business, education, medicine, psychology, public health and social science. Researchers and Statisticians using SEMs in biostatistics and in the social and behavioural sciences.

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Computing Effect Sizes for Evidence Synthesis for Decision Making in Meta-analysis Healthcare Michael Borenstein, Larry V. Hedges, Julian P. T. Higgins, Hannah R. Rothstein

Alexander J. Sutton, Keith R. Abrams, A.E Ades, Nicola J. Cooper, Nicky J. Welton

978-0-470-05847-3 / 0-470-05847-1 256 pp. Pub: 05/10/12 Biostatistics

978-0-470-06109-1 / 0-470-06109-X 320 pp. Pub: 25/05/12 Biostatistics

An advanced text dealing with a specialist, perennially vital part of meta-analysis, in an accessibly didactic style.

A team of expert authors present a unique computational approach to synthesising evidence from multiple sources.

There are many issues that researchers need to address in computing treatment effects, many of which have not been tackled thus far in the literature. This book comprehensively addresses all the relevant issues in computing treatment effects, including many not previously detailed in the literature. It examines the relations between the different kinds of treatment effects, and how to convert amongst them, detailing both correlational and continuous data. Introductions and worked examples are provided for all the formulas used and an accompanying website with an instructional version of a computer program that computes treatment effects, allows the reader to perform all the exercises in the book, and check the accuracy of any spreadsheets they constructed. •

• • • •

Evidence Syntesis for Decision Making intends to provide a practical guide to the appropriate methods for synthesising evidence for use in analytical decision models. More specifically, it proposes a comprehensive evidence synthesis framework, which models all the available data appropriately and efficiently in a format that can be incorporated directly into a decision model.

Comprehensively addresses all the relevant issues in computing treatment effects, including many not previously detailed in the literature. Examines the relations between the different kinds of treatment effects, and how to convert amongst them.

The first book to present a unified computational approach to evidence synthesis.

Presents evidence synthesis methods compatible with NICE (and equivalent international) guidelines.

Introduces the subject with an overview of Bayesian methods.

Emphasises the importance of model critique and checking for evidence consistency, as well as appropriate diagnostics and their application.

Adopts a probabilistic approach throughout, via the use of MCMC simulation.

Each chapter contains worked examples, exercises and solutions drawn from a variety of medical disciplines.

Based upon a tried and tested course presented by the authors.

Provides adaptable WinBUGS code via a website allowing readers to apply to their own analyses.

Details both correlational and continuous data. Provides introductions and worked examples for all the formulas used. Accompanying website with an instructional version of a computer program that computes treatment effects, allowing the reader to perform all the exercises in the book, and check the accuracy of any spreadsheets they constructed.

Written by respected authorities in the area, with a wealth of teaching experience.

Developed from three years research by the authors under a grant from the NIH.

Statisticians (specifically biostatisticians) wishing to learn about complex evidence synthesis methods, and users of decision models (e.g. policy makers). -Health economists, statisticians, operations researchers and decision modelers/makers undertaking health technology assessments.

Researchers and graduate students carrying out meta-analyses in medical research, psychology, and any discipline where meta-analysis takes place. Advanced undergraduate students and MSc students studying any subject in which meta-analysis is used, such as biostatistics, medicine, psychology, etc. University and Medical Libraries, Pharmaceutical companies, Government regulatory agencies. S p r i n g / S u m m e r

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Bayesian Methods in Biostatistics

Multiple Imputation and its Application

Emmanuel Lesaffre, Andrew B. Lawson

James Carpenter, Michael Kenward

978-0-470-01823-1 / 0-470-01823-2 320 pp. Pub: 20/07/12 Biostatistics

978-0-470-74052-1 / 0-470-74052-3 320 pp. Pub: 12/10/12 Biostatistics

This book provides an authoritative account of Bayesian methodology, from its most basic elements to its practical implementations, with an emphasis on healthcare techniques. Through examples, exercises, and both introductory and more advanced chapters, this book provides an invaluable understanding of the complex world of biomedical statistics.

A practical guide to the essential statistical tools needed to handle missing data in order from both observational studies and randomised trials

Part 1 provides an introduction into Bayesian methodology, common to all areas of Bayesian statistics, including Posterior Sampling and Computation. Part 2 expands on these methods and examines their applications within the field of biostatistics, with chapters on Clinical Trials, Longitudinal Studies, Survival Analysis, Errors-in-Measurement Models, Disease Mapping, Cross-Sectional and Cohort Studies, Bioinformatics, and Bioassay. An accompanying website allows the reader to replay the case studies and examples of implementations, allowing for a stronger understanding of the concepts. •

This book provides an authoritative account of Bayesian methodology, from its most basic elements to its practical implementations, with an emphasis on healthcare techniques.

Contains introductory explanations principles common to all areas.

Clear and concise examples in biostatistics applications including Clinical Trials, Survival Data, Longitudinal Analysis, Disease Mapping, Bioassay, Time Series, and Bioinformatics.

of

Imputation is the substitution of some value for a missing data point or a missing component of a data point. Once all missing values have been imputed, the dataset can then be analysed using standard techniques for complete data. This book is written with three main aims; to provide a thorough introduction to the general MI methods, to provide a detailed discussion of the practical use of the MI method and to present real-world examples drawn from the field of biostatistics. Illustrated throughout, using different issues that arise in the use of MI in observational and clinical trial settings. Relevant computer code and data will be provided for the examples used throughout the book and will include SAS, Stata, WinBUGS, MLwiN and R. •

Provides an introduction to general multiple imputation methods.

Discusses issues that arise with the use of MI in practical settings and recent developments

Illustrated throughout with real examples taken from the authors' vast experience.

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.

Bayesian

Both authors have considerable research and teaching experience in this area.

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

Illustrated examples using software including FirstBayes, WinBUGS and BayesX package.

Written by the leading authorities in the area.

Highlights the differences between the Bayesian and classical approaches.

Accompanying website hosting free software and case study guides.

Applied statisticians and researchers dealing with missing data problems in the medical and social sciences field. Academics and graduate students working in missing data

Senior undergraduates and taught post graduates in biostatistics. Statisticians and researchers in Bayesian statistics, with a focus on Biostatistics. Researchers in health institutes and the pharmaceutical industry. M a t h s & S t a t i s t i c s R i g h t s G u i d e

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Design and Analysis of Experiments in the Health Sciences

Statistical Methods in Healthcare Frederick Faltin, Ron Kenett, Fabrizio Ruggeri

Gerald van Belle, Kathleen F. Kerr

978-0-470-67015-6 / 0-470-67015-0 448 pp. Pub: 21/09/12 Quality, Productivity & Reliability

978-0-470-12727-8 / 0-470-12727-9 248 pp. Pub: 03/08/12 Clinical Trials

A comprehensive volume on statistical methods used in planning, delivering and monitoring health care, as well as the development and production of pharmaceuticals and medical devices.

This primer is an essential resource for professionals who need a hands-on book for designing and analysing laboratory, animal, and human experiments and scientific investigations in the health sciences. •

The principles and rules presented in this book apply very generally to most areas of research, such as clinical trials, agricultural investigations, industrial procedures, quality control procedures, and epidemiological studies.

Current laboratory work of fundamental interest, i.e. microarrays, genomics, proteomics, is discussed.

A feature of this primer is the close tie between design, analysis, and presentation of results.

References to texts, articles, and websites are included. A selected bibliography of experiments in industrial hygiene, occupational medicine, ergonomics, and environmental studies is also provided.

The historical context of the design of experiments is discussed, i.e. the increasing emphasis on clinical trials starting in the 1960's led to the development of survival analysis, database management, and appropriate computational procedures.

The book's related website (vanbelle.org) is freely accessible and contains most of the data sets used within the book, frequently in a format that can be easily imported into most statistical software.

Provides a comprehensive, in-depth treatment of statistical methods in healthcare.

Presents a reference source for practitioners and specialists in health care and drug development.

Offers a broad coverage of standards and established methods through leading edge techniques.

Uses an integrated, case- study based approach, with focus on applications.

Practitioners in healthcare-related professions (from clinical trials to medical devices design) with statistical backgrounds. Applied statistical methods researchers

Intended as a first resource for graduate students beginning to think about a thesis design; for biomedical researchers wanting a quick overview of criteria for grant applications; for environmental consultants discussing a project with a client; for all investigators interested in structured investigations; and physicians and biologists working in laboratories; and corporate and acadmic libraries. .

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Statistics for Finance, Business & Economics

Handbook of Volatility Models and Their Applications

Financial Statistics and Mathematical Finance: Methods, Models and Applications

Luc Bauwens, Christian M. Hafner, Sebastien Laurent 978-0-470-87251-2 / 0-470-87251-9 568 pp. Pub: 15/06/12 Financial Engineering

A. Steland

The main purpose of this handbook is to illustrate the mathematically fundamental implementation of various volatility models in the banking and financial industries, both at home and abroad, through use of real-world, time-sensitive applications. Conceived and written by over two-dozen experts in the field, the focus is to cohesively demonstrate how "volatile" certain statistical decision-making techniques can be when solving a range of financial problems. By using examples derived from consulting projects, current research and course instruction, each chapter in the book offers a systematic understanding of the recent advances in volatility modeling related to real-world situations. Every effort is made to present a balanced treatment between theory and practice, as well as to showcase how accuracy and efficiency in implementing various methods can be used as indispensable tools in assessing volatility rates. Unique to the book is in-depth coverage of GARCH-family models, contagion, and model comparisons between different volatility models.

978-0-470-71058-6 / 0-470-71058-6 352 pp. Pub: 20/07/12 Econometric & Statistical Methods A thorough and comprehensive text explaining the major statistical and mathematical concepts in mathematical finance. •

Provides an introduction to the basics of financial statistics and mathematical finance.

Explains the use and importance of statistical methods in econometrics and financial engineering.

Illustrates the importance of derivatives and calculus to aid understanding in methods and results.

Looks at advanced topics such as martingale theory, stochastic processes and stochastic integration.

The scope of the content is wider -- and deeper -- than any known competitor, including unique topics such as GARCH-family models, jumps in volatility and spillover effect (or contagion) in volatility, and model comparisons between different volatility models.

Extensive case studies and problems exhibit the real-world relevance of the discussed topics and methods.

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

An assortment of computer programs is employed, ranging from R, C++, EXCEL-VBA, Minitab, to JMP/SAS.

Examples used throughout to illustrate applications in mathematical and statistical finance.

Graduate students and researchers in statistics, finance, econometrics and business administration. Graduate students and researchers in financial engineering and computer science.

Practitioners in the fields of finance, business, applied statistics, econometrics, and engineering and for libraries in academic and corporate (including government) settings; as a supplement to courses on risk management and volatility in similar departments and business schools at the graduate and MBA level

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Understanding Business Research

Handbook of Exchange Rates

Bart L. Weathington, Christopher J. L. Cunningham, David J. Pittenger

Lucio Sarno, Jessica James, Ian Marsh 978-0-470-76883-9 / 0-470-76883-5 832 pp. Pub: 28/06/12 Statistics for Finance, Business & Economics

978-1-118-13426-9 / 1-118-13426-5 640 pp. Pub: 19/10/12 Statistics for Finance, Business & Economics

Featuring contributions from the world's leading researchers on the topic, this volume serves as a complete, timely, and authoritative guide to working with foreign exchange rates.

A comprehensive introduction to research methods and best practices for designing, conducting, interpreting, and reporting findings in business research. •

This book explores the research models, designs, and methodologies that are specific to carrying out research and facilitating decision-making in the field of business. The authors introduce various research designs that are appropriate for use in business and management science studies, including single participant, multi-group, longitudinal, correlational, and experimental designs.

Issues and steps common across all single-factor and multifactor studies are discussed, as well as single-subject and nonexperimental methods.

Throughout the book, examples accompany the discussed research designs, and Research in Action sections demonstrate the real-world uses of the presented topics.

Techniques realted to measurement are discussed in-depth, with guidance on identifying, developing, and evaluating reliable and valid approaches.

As a book for courses on business statistics and business research methods, and management research methods courses at the graduate/MBA level; as a reference for researchers and practitioners in areas of business, finance, and management science who utilise qualitative and quantitative research methods in applied scenarios

This handbook provides a collection of original ideas on foreign exchange rate and provides the necessary background on relevant concepts, risks, and policies for working in today's international economic climate.

This volume features contributions from leading international academic and practitioners in the field, providing a truly global perspective on practices and policies related to foreign exchange rates.

Outlining methodology, the book addresses relevant questions related to carrying out forecasting such as: How should we evaluate forecasting power of models? What are appropriate loss functions for major market participants?, and Is the exchange rate the only means of adjustment? Each chapter presents one topic and includes the following common pedagogy: chapter introductions, summaries, and references; historical notes; relevant theory and applications; and analysis methods and key formulae.

Clearly organised into five parts, the handbook provides a historical review of foreign exchange rates, methods for predicting foreign exchange rates, trading and hedging of the foreign exchanges, products and their pricings, and crises related to the foreign exchange rate markets.

Real examples from the finance industry are incorporated through the chapters, providing a balance between theory and practice in foreign exchange rates.

As a reference book on foreign exchange rates for fund managers, quantitative researchers, and practitioners working in financial markets, banking, and finance; as a supplement for courses on economics, business, and international finance at the upper-undergraduate and graduate levels; and academic and corporate libraries.

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A Modern Theory of Random Variation: With Applications in Stochastic Calculus, Financial Mathematics, and Feynman Integration

An Introduction to Analysis of Financial Data with R Ruey S. Tsay 978-0-470-89081-3 / 0-470-89081-9 416 pp. Pub: 19/10/12 Statistics for Finance, Business & Economics Re. Tsay's Financial Time Series (Copyright 2005) volume: "...too wonderful [a] book to be missed by any one who works in time series analysis." (Journal of Statistical Computation and Simulation, October 2006)

Patrick Muldowney 978-1-118-16640-6 / 1-118-16640-X 512 pp. Pub: 10/08/12 Statistics for Finance, Business & Economics

This book provides a systematic and mathematically accessible introduction to financial econometric models and their applications in modeling and predicting financial time series data. It emphasises empirical financial data and focuses on real-world examples. Following this approach, readers will master key aspects of financial time series, including volatility modeling, neural network applications, market microstructure, and high-frequency financial data. S-Plus® commands and illustrations are used extensively throughout the book in order to highlight accurate interpretations and graphical representations of financial data. Exercises are included in order to provide readers with more opportunities to put the models and methods into everyday practice. The tools provided in the text aid readers in developing a deeper understanding of financial markets through firsthand experience in working with financial data, most importantly without needless computation.

With a rigorous theorem-proof approach to stochastic models for financial mathematics as well as a unique focus on Feynman path integration, this book presents the theory of random processes and has applications in numerous areas including applied mathematics and statistics, finance, communication engineering, quantum mechanics, and physics. •

Uniquely focuses on the Henstock integral in probability and stochastic calculus with applications in option pricing and Feynman path integrals

Written by an expert in the field and details the mathematical modeling of phenomena in finance, communication engineering, quantum mechanics, and physics

Presents a new framework in which the Feynman path integrals are actual integrals that are used to express Feynman diagrams as a convergent series of integrals

• All of the essential content for beginning financial analysts, including new MBAs. •

Features an introduction to Brownian motion and uses Henstock's theory of generalised Reimann integration as the basis for simplifying the standard theory of probability and random variation

Simplified version of the author's authoritative (theory-driven) book on the same subject.

Includes various applications of mathematical and probabilistic methods and tools, such as option pricing, derivative asset valuation, and function integration

Coverage of newly developed financial models such as long memory, regime switching, structural break, and MCMC.

A plethora of real-life examples and empirical applications drawn from finance, international finance, and econometrics.

Fully integrated R software.

Lavish graphics to support the data.

An author web site with data sets, further explanations, and additional R subroutines.

Provides the needed notation and definitions as well as the technical background in advance of the fuller exposition of the main theorems and proofs

As a reference and self-study for financial and business investment professionals as well as applied mathematicians who need to reinforce their analytical skills; also appropriate for professionals who work in the theoretical side of quantum mechanics; as a graduate-level textbook for courses in measure and integration, differential equations, financial calculus, and probability theory; and academic and corporate libraries. M a t h s & S t a t i s t i c s R i g h t s G u i d e

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This is an ideal textbook for MBA students, as well as a reference for researchers and professionals in business and finance.

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Statistical Methods in Customer Relationship Management

Statistics for Social Sciences The Visualisation of Spatial Social Structure

Viba Kumar, Andrew Petersen 978-1-119-99320-9 / 1-119-99320-2 352 pp. Pub: 20/07/12 Statistics for Finance, Business & Economics

Danny Dorling 978-1-119-96293-9 / 1-119-96293-5 288 pp. Pub: 31/08/12 Statistics for Social Sciences

Focuses on the quantitative tools necessary for effective customer relationship management •

Presents an overview of a customer relationship management (CRM) system, introducing key concepts and metrics needed to understand and implement CRM models.

Focuses on five CRM models: customer acquisition, customer retention, customer churn, and customer win back with supported case studies.

Explores each model in detail, from investigating the need for CRM models to looking at the future of the models.

Lavishly illustrated with full colour graphics throughout, Dorling's unique approach shows how statistical data visualization techniques can enhance social science data

Presents models and concepts that span across the introductory, advanced, and specialist levels.

Academics and practitioners interested and/or involved in the area of CRM as well as instructors of applied statistics and quantitative marketing courses. Graduate students specializing in marketing, statistics and business as well as business consulting firms.

Illustrated in full colour throughout with fascinating colour graphic and animations.

Authored by a well-recognised, leading academic and prolific author, respected both in the fields of both statistics and human geography as well as within the mainstream media.

Focuses on visual representation of spatial social structure, illustrating how statistical data visualisation techniques can be applied to social science data.

Presents numerous case studies, such as a visual exploration of house price change across several years and thousands of places with new techniques developed to show the structure of local housing markets

Offers a single comprehensive and accessible overview of graphic display techniques.

Considers smaller scale analysis, looking at what many images can tell us about the distribution of a disease, viewed from many different directions in space and time.

Includes a bibliography of over 1500 references.

Advanced students, postgraduates and researchers in social science, demography, geography and statistics. Computer science and graphics "techies" and practitioners in all the above fields.

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Understanding and Applying Research Design

Agent-Based Computational Sociology

Martin Lee Abbott

Flaminio Squazzoni

978-1-118-09648-2 / 1-118-09648-7 384 pp. Pub: 13/12/12 Statistics for Social Sciences

978-0-470-71174-3 / 0-470-71174-4 248 pp. Pub: 11/05/12 Statistics for Social Sciences

This book presents an introductory treatment of research methods that integrates statistical approaches with research design elements, featuring real-world data treatments with SPSS(R) software.

An advanced practical guide to agent-based models in computational sociology.

Although research design and statistics are typically treated separately in the current literature, this book uniquely blends the two topics under one cover, identifying the relationship between the two and illustrating the practical application of these concepts.

Three clearly-organised sections explore the relevance of quantitative techniques in the research design framework, outlining topics such as measurement, causation and causal models, correlation, and regression analysis.

The authors develop theoretical research design content in a format that integrates exercises and worksheets, allowing readers to examine real world data from various fields of research.

Throughout the book, real-world problems illustrate the power of SPSS® to solve existing research design problems.

Most of the intriguing social phenomena of our time, such as international terrorism, social inequality, and urban ethnic segregation, are consequences of complex forms of agent interaction that are difficult to observe methodically and experimentally. This book looks at a new research stream that makes use of advanced computer simulation modelling techniques to spotlight agent interaction that allows us to explain the emergence of social patterns. It presents a method to pursue analytical sociology investigations that look at relevant social mechanisms in various empirical situations, such as markets, urban cities, and organisations.

Data from the authors' own research as well as key resources such as the General Social Survey (GSS) demonstrate the nature of and challenges that exist in designing and carrying out research studies.

As a book for upper-undergraduate and beginning graduate level courses on research methods in departments of sociology, public health, education, psychology, and the social sciences; as a reference for researchers and practitioners working in any area of the social sciences who need a basic understanding of the relationship between statistics and best research design practices.

M a t h s & S t a t i s t i c s R i g h t s G u i d e

Provides a comprehensive introduction to epistemological, theoretical and methodological features of agent-based modelling in sociology through various discussions and examples.

Presents the pros and cons of using agent-based models in sociology.

Explores agent-based models in combining quantitative and qualitative aspects, and micro- and macro levels of analysis.

Looks at how to pose an agent-based research question, identifying the model building blocks, and how to validate simulation results.

Features examples of agent-based models that look at crucial sociology issues.

Supported by an accompanying website featuring data sets and code for the models included in the book.

Agent-Based Computational Sociology is written in a common sociological language and features examples of models that look at all the traditional explanatory challenges of sociology. Researchers and graduate students involved in the field of agent-based modelling and computer simulation in areas such as social sciences, cognitive sciences and computer sciences will benefit from this book.

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Using the Weibull Distribution: Reliability, Modeling and Inference

Statistics for Engineering Industrial Statistics with Minitab

John I. McCool

Pere Grima

978-1-118-21798-6 / 1-118-21798-5 384 pp. Pub: 12/10/12 Quality, Productivity & Reliability

978-0-470-97275-5 / 0-470-97275-0 448 pp. Pub: 31/08/12 Engineering Statistics

This book presents the theory, statistical background, and specialised software, including R, for understanding newly-evolving applications of the Weibull distrbution across various fields of study.

An example-rich introduction to industrial statistics using Minitab. •

Contains extensive illustrative examples and case studies throughout.

Features Statistical Process Control, Multi-Vari Charts and creating and analysing factorial designs.

Discusses Data Handling and customisation with MINITAB.

Based on a successful training course delivered by the authors to both students and practitioners.

Supported by an accompanying website featuring case studies.

Technical and engineering students, Six Sigma Green and Black Belts and middle managers dealing with processes, productivity, quality or related topics. Engineering students and practitioners from industry interested in learning statistics.

This book is a major step forward in the current literature on Weibull inferential methods, outlining important contributions in a style tailored for readers with minimal statistical background.

Examples throughout the book stem from the field of engineering, but the discussed concepts can be applied to various areas of study including business, biostatistics, and operations research.

All analyses are presented using R as well as the author's own software, which is freely-available on the book's related Web site.

The author introduces newly-evolving topics that are missing from the current literature on the topic, including analysis of one way classification experiments with Weibull response, multiple comparison tests for the equality of k Weibull shape and scale parameters, and exact inference based on the results of sudden death tests.

A brief discussion is included on alternative models, shedding light on the importance and benefits of the Weibull distribution in the context of other techniques.

The book's material has been class-tested by the author, ensuring an accessible and fluid presentation.

Problem sets are provided throughout the book, allowing readers to test their understanding of the presented material.

No Spanish language rights

As a book for courses on applied statistics and reliablility engineering at the upper-undergraduate and graduate levels; as a reference for statisticians, engineers, business analysts, operation research professionals, and risk analysts who gather and interpret data that follows the Weibull distribution

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Statistics for Scientists and Engineers R. Chattamvelli 978-1-118-22896-8 / 1-118-22896-0 800 pp. Pub: 27/08/12 Engineering Statistics This comprehensive book covers descriptive statistics with an emphasis on scientific and engineering applications. The book brings to light new programming ideas, algorithms and equations, and unlike nearly all available books in the field, deals exclusively with descriptive statistics. •

Includes end-of-chapter exercises and recommended reading lists for each chapter

Includes data sets and illustrative examples

New algorithms and available anywhere else

Includes section on dynamic data dependent systems like industrial robots, unmanned aerial vehicles (UAV) and pilot-less machines

Provides insight on coding complex algorithms using the "loop unrolling technique"

equations

that

are not

Professionals and researchers in software development for engineering applications. Graduate and advanced undergraduate students in engineering, computer and information sciences. Professionals in astronomical sciences, medical sciences, statistics, management and physical sciences.

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Maths and Statistics Rights Guide Spring/Summer 2012  

Maths and Statistics Rights Guide Spring/Summer 2012

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