Maths & Statistics: Right Guides 2014 Mathematics ......................................................................... 4 Fractal Geometry: Mathematical Foundations and Applications/Falconer..................................... 4 Introduction to Imprecise Probabilities/Augustin.............................................................................. 4 Lower Previsions/Troffaes .................................................................................................................. 5 Theory of Computational Complexity, 2e/Du .................................................................................... 5 Understanding Uncertainty, 2e/Lindley .............................................................................................. 6 Beginning Partial Differential Equations, 3e/O'Neil .......................................................................... 6 Classical Geometry: Euclidean, Transformational, Inversive, and Projective/Leonard ................ 7 Solutions Manual to Accompany Classical Geometry: Euclidean, Transformational, Inversive, and Projective/Leonard ....................................................................................................................... 7 Combinatorial Reasoning: An Introduction to the Art of Counting/DeTemple .............................. 8 Solutions Manual to Accompany Combinatorial Reasoning: An Introduction to the Art of Counting/DeTemple ............................................................................................................................. 8 The Development of Mathematics Throughout the Centuries: A Brief History in a Cultural Context/Evans...................................................................................................................................... 9 Examples and Problems in Mathematical Statistics/Zacks ............................................................. 9 Introduction to Numerical Methods for Time Dependent Differential Equations/Kreiss ............. 10 Handbook of Probability/Florescu .................................................................................................... 10
Medical and Biostatistics .................................................. 11 Clinical Trials with Missing Data: A Guide for Practitioners/O'Kelly ............................................. 11 A Practical Guide to Designing Phase II Trials in Oncology/Brown .............................................. 11 Statistical Modelling of ICU Data/Chevret........................................................................................ 12 Applied Missing Data Analysis in the Health Sciences/Zhou ........................................................ 12 Exploration and Analysis of DNA Microarray and Other High-Dimensional Data, 2e/Amaratunga ............................................................................................................................................................ 13 Design and Analysis of Clinical Trials: Concepts and Methodologies, 3e/Chow ........................ 13 Differential Equation Analysis in Biomedical Science and Engineering: Partial Differential Equation Applications with R/Schiesser .......................................................................................... 14 Differential Equation Analysis in Biomedical Science and Engineering: Ordinary Differential Equation Applications with R/Schiesser .......................................................................................... 14 Explorations of Mathematical Models in Biology with MATLAB/Shahin ...................................... 15 Spatial and Temporal Dynamics of Infectious Diseases/Chen...................................................... 15 Applied Bayesian Modelling, 2e/Congdon ....................................................................................... 16 How to Design, Analyse and Report Cluster Randomised Trials in Medicine and Health Related Research/Campbell............................................................................................................................ 16
Statistics for Engineering ................................................. 17 Modern Industrial Statistics: with applications in R, MINITAB and JMP, 2e/Kenett .................... 17 Statistical Robust Design: An Industrial Perspective/Arner .......................................................... 17 M a t h s
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Uncertainty in Risk Assessment: The Representation and Treatment of Uncertainties by Probabilistic and Non-Probabilistic Methods/Aven ........................................................................ 18 Reliability of Safety-Critical Systems: Theory and Applications/Rausand ................................... 18 Solutions Manual to Accompany Statistics and Probability with Applications for Engineers and Scientists, Solutions Manual/Gupta ......................................................................................... 19
Environmental Statistics ................................................... 19 Statistical Applications for Environmental Analysis and Risk Assessment/Ofungwu ................ 19
General Statistics .............................................................. 20 Statistical Hypothesis Testing with SAS and R /Taeger ................................................................ 20 Understanding Large Temporal Networks and Spatial Networks: Exploration, Pattern Searching, Visualization and Network Evolution/Doreian.............................................................. 20 Nonparametric Hypotheses Testing with R - Rank: Tests and Permutation Tests/Bonnini ........ 21 Online Panel Research: A Data Quality Perspective/Callegaro ..................................................... 21 Propagation Dynamics on Complex Networks: Models, Methods and Stability Analysis/Fu .... 22 Statistical Analysis in Forensic Science: Evidential Values of Multivariate Physicochemical Data/Zadora ........................................................................................................................................ 22 Design, Evaluation, and Analysis of Questionnaires for Survey Research, 2e/Saris .................. 23 Multivariate Nonparametric Regression and Visualization: With R and Applications to Finance/Klemelä ....................................................................................................................... 23 Applied Linear Regression, 4e/Weisberg......................................................................................... 24 Fast Sequential Monte Carlo Methods for Counting and Optimization/Rubinstein ...................... 24 Growth Curve Modeling: Theory and Applications/Panik .............................................................. 25 Nonparametric Statistical Methods, 3e/Hollander ........................................................................... 25 Nonparametric Statistics: A Step-by-Step Approach, 2e/Corder .................................................. 26 Repeated Measurements and Cross-Over Designs/Raghavarao................................................... 26 Statistical Inference for Models with Multivariate t-Distributed Errors/Saleh .............................. 27
Statistics for Finance and Business ................................ 27 Business Risk Management: Models and Analysis/Anderson ....................................................... 27 Discrete-Event Simulation and System Dynamics for Management Decision Making/Brailsford ............................................................................................................................................................ 28 Practical Data Mining for Business: Case Studies and Methodology/Ahlemeyer-Stubbe ........... 28 Panel Data Analysis using EViews/Agung....................................................................................... 29 Quantile Regression: Theory and Applications/Davino ................................................................. 29 Handbook in Monte Carlo Simulation: Applications in Financial Engineering, Risk Management, and Economics/Brandimarte ..................................................................................... 30 Handbook of Market Risk/Szylar ...................................................................................................... 30 Multivariate Time Series Analysis: With R and Financial Applications/Tsay ............................... 31 Measure, Probability, and Mathematical Finance: A Problem-Oriented Approach/Gan ............. 31
Statistics for Social Science ............................................. 32 Analytical Sociology: Actions and Networks/Manzo ..................................................................... 32 S p r i n g / S u m m e r
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Computational Approaches to Studying the Co-evolution of Networks and Behavior in Social Dilemmas/Corten ............................................................................................................................... 32 Bayesian Inference in the Social Sciences/Jeliazkov ..................................................................... 33 Statistics for Exercise Science and Health with Microsoft Office Excel/Verma........................... 33 The Wellbeing of Nations: Meaning, Motive and Measurement/Allin ............................................ 34 Social Networks and their Economics: Influencing Consumer Choice/Birke .............................. 34
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Introduction to Imprecise Probabilities
Fractal Geometry: Mathematical Foundations and Applications
Thomas Augustin, Frank P. A. Coolen, Gert de Cooman, Matthias C. M. Troffaes 978-0-470-97381-3 / 0-470-97381-1 448 pp. Pub: 14/04/14 Probability & Mathematical Statistics
Kenneth Falconer 978-1-119-94239-9 / 1-119-94239-X 400 pp. Pub: 25/02/14 Chaos / Fractal / Dynamical Systems
An Introduction to Imprecise Probabilities provides a comprehensive introduction to imprecise probabilities, including theory and applications reflecting the current state if the art. Each chapter is written by experts on the respective topics, including: Sets of desirable gambles; Coherent lower (conditional) previsions; Special cases and links to literature; Decision making; Graphical models; Classification; Reliability and risk assessment; Statistical inference; Structural judgments; Aspects of implementation (including elicitation and computation); Models in finance; Game-theoretic probability; Stochastic processes (including Markov chains); Engineering applications.
Widely regarded as the best textbook on the subject, and considered a standard reference by researchers, this seminal text on fractal geometry for students and researchers: extensively revised and updated with new material, notes and references that reflect recent directions. Fractal Geometry: Mathematical Foundations and Applications is an excellent course book for undergraduate and graduate students studying fractal geometry, with suggestions for material appropriate for a first course indicated. The book also provides an invaluable foundation and reference for researchers who encounter fractals not only in mathematics but also in other areas across physics, engineering and the applied sciences.
Essential reading for researchers in academia, research institutes and other organizations, as well as practitioners engaged in areas such as risk analysis and engineering. • The first book to chart the development and applications of this growing subject.
• Provides a comprehensive and accessible introduction to the mathematical theory and applications of fractals.
• Provides a comprehensive introduction to imprecise probabilities, including theory and applications reflecting the current state of the art.
• Carefully explains each topic using illustrative examples and diagrams.
• Each chapter is written by leading experts in their field.
• Includes the necessary mathematical background material, along with notes and references to enable the reader to pursue individual topics.
• Is supported by a website featuring developed software for the implementation of the methods featured in the book.
• Features a wide range of exercises, enabling readers to consolidate their understanding.
Researchers in academics, research institutes and other organizations. Practitioners with strong mathematical ability in many fields, e.g. risk analysts, engineers, applied statisticians.
• Supported by a website with solutions to exercises and additional material http://www.wileyeurope.com/fractal.
Thomas Augustin, Department of Statistics, University of Munich, Germany. Frank Coolen, Department of Mathematical Sciences, Durham University, UK. Gert de Cooman, Research Professor in Uncertainty Modelling and Systems Science, Ghent University, Belgium. Matthias Troffaes, Department of Mathematical Sciences, Durham University, UK.
Leads onto the more advanced sequel Techniques in Fractal Geometry (also by Kenneth Falconer and available from Wiley). Senior undergraduate and masters students studying courses in fractal geometry. Researchers working with fractals from mathematics, engineering, physics, and the applied sciences. Kenneth Falconer, Mathematical Institute, University of St Andrews, UK. Fractal Geometry - Mathematical Foundations & Applications – Licensed: French, German, Japanese. Fractal Geometry - Mathematical Foundations and Applications 2e Licensed: Japanese, Simplified Chinese. S p r i n g / S u m m e r
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Theory of Computational Complexity, 2e
Matthias C. M. Troffaes, Gert de Cooman
Ding-Zhu Du, Ker-I Ko
978-0-470-72377-7 / 0-470-72377-7 432 pp. Pub: 21/07/14 Probability & Mathematical Statistics
978-1-118-30608-6 / 1-118-30608-2 472 pp. Pub: 21/07/14 Chaos / Fractal / Dynamical Systems
Extends the classic theory of lower previsions to deal with unbounded quantities, often found in optimization problems.
This new edition highlights advances in the field of computational complexity, including newly-developed algorithms and novel applications to quantum computing.
This book has two main purposes. On the one hand, it provides a concise and systematic development of the theory of lower previsions, based on the concept of acceptability, in spirit of the work of Williams and Walley. On the other hand, it also extends this theory to deal with unbounded quantities, which abound in practical applications.
Maintaining extensive and detailed coverage, Theory of Computational Complexity, Second Edition examines the theory and methods behind complexity theory, such as computational models, decision tree complexity, circuit complexity, and probabilistic complexity. The Second Edition also features recent developments on areas such as NP-completeness theory, as well as:
• Illustrates ways in which the theory of Lower Previsions can be extended to cover a larger set of random quantities.
• A new combinatorial proof of the PCP theorem based on the notion of expander graphs, a research area in the field of computer science.
• 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.
• Additional exercises at varying levels of difficulty to further test comprehension of the presented material. • End-of-chapter literature reviews that summarize each topic and offer additional sources for further study.
Theory of Computational Complexity, Second Edition is an excellent textbook for courses on computational theory and complexity at the graduate-level. The book is also a useful reference for practitioners in the fields of computer science, engineering, and mathematics who utilize state-of-the-art software and computational methods to conduct research.
• 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.
Ding-Zhu Du, PhD, Professor, Department of Computation Science, University of Texas, has published over 200 journal articles in his areas of research interest. Dr. Du is coauthor of Problem Solving in Automata, Languages, and Complexity, also published by Wiley. Ker I. Ko, PhD, is Professor in the Department of Computer Science at Stony Brook University. He has published extensively in his areas of research interest, which include computational complexity, theory of computation, computational learning theory. Dr. Ko is Associate Editor of Journal of Complexity and the coauthor of Problem Solving in Automata, Languages, and Complexity, also published by Wiley.
Researchers and practitioners working in statistics, mathematics, engineering, artificial intelligence and decision theory. Researchers and students interested in (imprecise) probabilities and unbounded quantities. Matthias Troffaes -- Department of Mathematical Sciences, Durham University, UK. Since gaining his PhD, Dr Troffaes has conducted research in Belgium and the US in imprecise probabilities, before becoming a lecturer in statistics at Durham. He has published papers in a variety of journals, and written two book chapters. Gert de Cooman -- SYSTeMS Research Group, Ghent University, Belgium. With many years' research and teaching experience, Professor de Cooman serves/has served on the Editorial Boards of many statistical journals. He has published over 40 journal articles, and is an editor of the Imprecise Probabilities Project. He has also written chapters for six books, and has co-edited four. M a t h s
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Understanding Uncertainty, 2e
Beginning Partial Differential Equations, 3e
Dennis V. Lindley
Peter V. O'Neil
978-1-118-65012-7 / 1-118-65012-3 424 pp. Pub: 24/01/14 Probability & Mathematical Statistics
978-1-118-62994-9 / 1-118-62994-9 456 pp. Pub: 25/02/14 Differential Equations
The critically acclaimed First Edition of Understanding Uncertainty provided a study of uncertainty addressed to scholars in all fields, showing that uncertainty could be measured by probability, and that probability obeyed three basic rules that enabled uncertainty to be handled sensibly in everyday life. These ideas were extended to embrace the scientific method and to show how decisions, containing an uncertain element, could be rationally made.
This new edition features a broad introduction to partial differential equations, while also addressing more specialized topics and applications that occur throughout the field of mathematics as well as within engineering and the physical and life sciences. Featuring a thoroughly revised presentation of topics, it provides a challenging, yet accessible, combination of techniques, applications, and introductory theory on the subject of partial differential equations. The new edition offers nonstandard coverage on material including Burger's equation, the telegraph equation, damped wave motion, and the use of characteristics to solve nonhomogeneous problems.
Featuring new material, the Revised Edition remains the go-to guide for uncertainty and decision making, providing further applications at an accessible level including: • A critical study of transitivity, a basic concept in probability.
• Proofs of theorems incorporated within the topical presentation, such as the existence of a solution for the Dirichlet problem.
• A discussion of how the failure of the financial sector to use the proper approach to uncertainty may have contributed to the recent recession.
• The incorporation of Maple to perform computations and experiments.
• A consideration of betting, showing that a bookmaker's odds are not expressions of probability.
• Unusual applications, such as Poe's pendulum.
• Applications of the book's thesis to statistics.
• Advanced topical coverage of special functions, such as Bessel, Legendre polynomials, and spherical harmonics.
• A demonstration that some techniques currently popular in statistics, like significance tests, may be unsound, even seriously misleading, because they violate the rules of probability.
• Fourier and Laplace transform techniques to solve important problems. As a textbook for advanced undergraduate and/or first-year graduate-level courses in mathematics (applied math and analysis), science, and engineering; and academic and departmental libraries. The book's mathematical details makes it attractive to mathematics departments, while the techniques of solution, problem-solving, and selected applications makes it useful to science and engineering departments.
Requiring only a basic understanding of mathematical concepts and operations, Understanding Uncertainty, Revised Edition is useful as a text or supplement for all students who have probability or statistics as part of their course, even at the most introductory level. Dennis V. Lindley is Professor Emeritus of Statistics, and past Head of Department, at University College London (UK). He has played a leading role in putting Bayesian statistics back on the modern statistical map. He has published over 100 original and significant scholarly articles, as well as several books, that are all delightfully written and full of insights. He is a founding organizer and former president of the celebrated Valencia International Meetings on Bayesian Statistics, the 2002 meeting of which was dedicated in his honor.
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Peter V. O'Neil, PhD, is Professor Emeritus in the Department of Mathematics at The University of Alabaman at Birmingham. He is a Member of the American Mathematical Society, Mathematical Association of America, Society for Industrial and Applied Mathematics, and the American Association for the Advancement of Science.
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Classical Geometry: Euclidean, Transformational, Inversive, and Projective
Solutions Manual to Accompany Classical Geometry: Euclidean, Transformational, Inversive, and Projective
Ed Leonard, J. E. Lewis, A. C. F. Liu, G. Tokarsky
Ed Leonard, J. E. Lewis, A. C. Liu, G. Tokarsky
978-1-118-67919-7 / 1-118-67919-9 320 pp. Pub: 28/04/14 Geometry & Topology
978-1-118-90352-0 / 1-118-90352-8 192 pp. Pub: 28/04/14 Geometry & Topology
Written by well-known mathematical problem solvers, this book features up-to-date and applicable coverage of the wide spectrum of modern geometry and aids readers in learning the art of logical reasoning, modeling, and proof. With its reader-friendly approach, this undergraduate text features self-contained topical coverage and provides a large selection of solved exercises to aid in reader comprehension. Material in this text can be tailored for a one-, two-, or three-semester sequence. • Addresses plane Euclidian geometry, transformational geometry, and inversive and projective geometry, all of which are applicable to readers with backgrounds in mathematics, education, engineering, and science. • Features self-contained coverage of modern geometry, provides a large selection of solved exercises to aid in reader comprehension, and contains material that can be tailored for a one-, two-, or three-semester sequence. • Written with a reader-friendly approach and provides a wide range of fully-worked exercises throughout. An Instructor's Solution Manual is available upon request to the Publisher. • Introduces topics by using examples as opposed to a purely theoretical approach, allowing the material to be more appealing and understandable to readers.
Solutions Manual to accompany Classical Geometry: Euclidean, Transformational, Inversive, and Projective. Written by well-known mathematical problem solvers, Classical Geometry: Euclidean, Transformational, Inversive, and Projective features up-to-date and applicable coverage of the wide spectrum of geometry and aids readers in learning the art of logical reasoning, modeling, and proof. With its reader-friendly approach, this undergraduate text features self-contained topical coverage and provides a large selection of solved exercises to aid in reader comprehension. Material in this text can be tailored for a one-, two-, or three-semester sequence.
As a textbook for courses in introductory geometry, elementary geometry, modern geometry, or history of mathematics at the undergraduate level for mathematics majors as well as engineering and secondary education majors. Ed Leonard, PhD, Lecturer, Department of Mathematical and Statistical Sciences, University of Alberta. The author of over 15 peer reviewed journal articles, he is a technical editor for the Canadian Applied Mathematical Quarterly journal. James E. Lewis, PhD, Professor Emeritus, Dept. of Mathematical Sciences, University of Alberta. Andrew C. F. Liu, PhD, Professor, Dept of Mathematical and Statistical Sciences, University of Alberta. William Tokarsky, PhD, is Faculty Lecturer in the Department of Mathematical and Statistical Sciences at the University of Alberta. M a t h s
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Combinatorial Reasoning: An Introduction to the Art of Counting
Solutions Manual to Accompany Combinatorial Reasoning: An Introduction to the Art of Counting
Duane DeTemple, William Webb
978-1-118-65218-3 / 1-118-65218-5 416 pp. Pub: 14/04/14 Combinatorics
978-1-118-83078-9 / 1-118-83078-4 192 pp. Pub: 12/05/14 Combinatorics
Written by well-known scholars in the field, this book introduces combinatorics alongside modern techniques, showcases the interdisciplinary aspects of the topic, and illustrates how to problem solve with a multitude of exercises throughout.
This is a solutions manual to accompany Combinatorial Reasoning: An Introduction to the Art of Counting. Written by well-known scholars in the field, Combinatorial Reasoning: An Introduction to the Art of Counting introduces combinatorics alongside modern techniques, showcases the interdisciplinary aspects of the topic, and illustrates how to problem solve with a multitude of exercises throughout. The authors' approach is very readerfriendly and avoids the "scholarly tone" found in many books on this topic.
• Focuses on enumeration and combinatorial thinking as a way to develop a variety of effective approaches to solving counting problems. • Includes brief summaries of basic concepts from probability, power series, and group theory to show how combinatorics interacts with other fields. • Provides abstract ideas that are grounded in familiar concrete settings and features plentiful diagrams throughout to further add in reader understanding. • Presents simple and helpful notations as needed, and simple cases are treated first before more general and/or advanced cases. • Contains over 700 exercise sets, ranging from the routine to the advanced, with hints, short answers, or complete solutions for odd numbered problems. An Instructor's Manual (available via request to the Publisher) provides complete solutions for all exercises. A primary textbook for introductory combinatorics and discrete mathematics courses at the upper-undergraduate and graduate levels. Duane DeTemple, PhD, is Professor Emeritus in the Department of Mathematics at Washington State University. He is the recipient of the 2007 WSU Sahlin Faculty Excellence Award for Instruction as well as the Distinguished Teaching Award from the Pacific Northwest Section of the MAA. William Webb, PhD, is Professor in the Department of Mathematics at Washington State University. Dr. Webb is President of the Fibonacci Association and focuses his research on the properties of recurrence sequences and binomial coefficients.
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The Development of Mathematics Throughout the Centuries: A Brief History in a Cultural Context
Examples and Problems in Mathematical Statistics Shelemyahu Zacks 978-1-118-60550-9 / 1-118-60550-0 652 pp. Pub: 17/03/14 Probability & Mathematical Statistics
This book presents examples that illustrate the theory of mathematical statistics and details how to apply the methods for solving problems. While other books on the topic contain problems and exercises, they do not focus on problem solving. This book fills an important niche in the statistical theory literature by providing a theory/example/problem approach. Chapter coverage includes: Basic Probability Theory; Statistical Distributions; Sufficient Statistics and Information in Samples; Testing Statistical Hypothesis; Statistical Estimation; Confidence and Tolerance Intervals; Large Sample Theory for Estimation and Testing; Bayesian Analysis in Testing and Estimation; and Advanced Topics in Estimation Theory.
978-1-118-85384-9 / 1-118-85384-9 240 pp. Pub: 26/05/14 History of Mathematics This book addresses the history of mathematics and features global cultural contributions that have influenced the development of mathematics as a coherent discipline. Throughout the book, readers take a journey throughout time and observe how people around the world have understood these patterns of quantity, structure, and dimension around them. The Development of Mathematics Throughout the Centuries: A Brief History in a Cultural Contex provides a brief overview of the history of mathematics in a very straightforward and understandable manner and also addresses major findings that influenced the development of mathematics as a coherent discipline. This book:
• Emphasizes problem solving and strikes an even balance between the theory, examples, and exercises. • Written in a very clear and detailed style and features practical and interesting examples throughout to help readers become proficient in theoretical problem solving. • Features 200 examples from a variety of fields including engineering, mathematics, and statistics and over 300 problems with select solutions.
• Highlights the contributions made by various world cultures including African, Egyptian, Babylonian, Chinese, Indian, Islamic, and pre-Columbian American mathematics.
• Provides the need statistical theory to problem solve as well as establish the related notations and proven results.
• Features an approach that is not too rigorous and is ideal for a one-semester course of the history of mathematics.
• Presents each chapter in a theory/examples/problems format to aid in reader comprehension.
• Includes a Resources and Recommended Reading section for further exploration and has been extensively classroom-tested.
• Supplemented with sample exam questions and additional solutions.
• Emphasizes the historical foundations and background of the history of mathematics in order to provide context in an easy to digest manner, all without a heavy focus on complicated mathematics.
As a textbook for inferential statistics, probability, and mathematical statistics courses at the graduate and PhD levels; as a reference for graduate students preparing for doctoral research; as a resource applied statisticians and researchers who need a refresher on the subject matter; and academic libraries.
As a textbook for undergraduate courses on the history of mathematics; also appropriate as a text for elementary and secondary education majors; professional and trade audiences interested in the history of mathematics; and academic and public libraries.
Shelemyahu Zacks, PhD, is Professor of Statistics in the Department of Mathematical Sciences at Binghamton University. He has published several books and over 170 journal articles in the areas of design and analysis of experiments, statistical control of stochastic processes, statistical decision theory, statistical methods in logistics, and sampling from finite populations. Dr. Zacks is a Fellow of the American Statistical Association, Institute of Mathematical Sciences, and American Association for the Advancement of Sciences.
Brian R. Evans, Ed.D., is Department Chair and Associate Professor in the School of Education at Pace University. He has authored over 25 journal articles and several book chapters. He is a member of the National Council of Teachers of Mathematics, Teachers without Borders, and the Mathematical Association of America. M a t h s
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Introduction to Numerical Methods for Time Dependent Differential Equations
Applied Probability & Statistics Handbook of Probability Ionut Florescu, Ciprian A. Tudor
Heinz-Otto Kreiss, Omar Eduardo Ortiz
978-0-470-64727-1 / 0-470-64727-2 472 pp. Pub: 02/12/13 Applied Probability & Statistics
978-1-118-83895-2 / 1-118-83895-5 192 pp. Pub: 21/04/14 Numerical Methods
This handbook provides a complete, but accessible compendium of all the major theorems, applications, and methodologies that are necessary for a clear understanding of probability.
This introductory, self-contained book emphasizes both the fundamentals of time-dependent differential equations and the numerical solutions of these equations.
The book provides a useful format with self-contained chapters, allowing the reader easy and quick reference. Each chapter includes an introduction, historical background, theory and applications, algorithms, and exercises.
The book is divided into two parts: Part One deals with ordinary differential equations (ODE) and their approximations. Part Two addresses partial differential equations in one space dimension and their approximations. Topical coverage includes: first order scalar equations; the method of Euler; higher order methods; the implicit Euler methods, two step and multistep methods; systems of differential equations; Fourier series and interpolation; 1-periodic solutions; approximations of 1-periodic solutions; linear initialboundary value problems; and nonlinear problems.
• The book is written by two eminent researchers in the field. • Plentiful pedagogy is provided for easy access and reference. • Chapters are self-contained, but comprehensive in scope.
• Provides topical coverage in a very simplified manner and only in a one space dimension.
• Historical notes are included in order to provide contextual background for key concepts.
• Presents the analytic theory and translates it into a theory for difference approximations.
• Applications are employed in an effort to expand upon the relevance of the subject matter.
• Contains worked out solutions to select answers at the end of the book.
As a resource book for graduate students, researchers and practitioners in all the sciences, most notably mathematics, statistics, operations research, engineering, medicine, and finance; as a reference book in all libraries (academic, public, and corporate)
• Offers an Instructor's Solution Manual containing the complete solutions (available via written request to the Publisher). • Classroom-tested and based on course notes used at both UCLA and the National University of Cordoba.
Ionut Florescu, PhD, is Assistant Professor of Mathematics at Stevens Institute of Technology in Hoboken, New Jersey. He has published extensively in his areas of research interest which include stochastic volatility, stochastic partial differential equations, Monte Carlo Methods, and numerical methods for stochastic processes. Currently, he focuses his teaching on probability and stochastic processes and their applications to finance, portfolio theory, and investments. Ciprain A. Tudor, PhD, is University Professor of Economics at The Sorbonne. His research interests include Brownian motion, limit theorems, statistical inference for stochastic processes, and financial mathematics. He serves as a referee for over a dozen journals, and he has been an accomplished speaker at over two-dozen conferences worldwide.
As a textbook for upper-undergraduate students of applied mathematics, engineering, physics, and scientific computations; as a reference for physical scientists, engineers, numerical analysts, and mathematical modelers who use numerical experiments to test designs and/or to predict and investigate physical phenomena; and academic and corporate libraries. Heinz-Otto Kriess, PhD, is Professor Emeritus in the Department of Mathematics at the University of California Los Angeles. Omar Educardo Ortiz, PhD, is Professor in the Department of Mathematics, Astronomy, and Physics at the University of Cordoba in Argentina.
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Medical and Biostatistics
A Practical Guide to Clinical Trials with Missing Designing Phase II Trials in Oncology Data: A Guide for Sarah R. Brown, Walter M. Gregory, Practitioners Christopher J. Twelves, Marc E. Buyse, Mahesh K. Parmar, Matthew T. Seymour, Julia M. Brown
Michael O'Kelly, Bohdana Ratitch 978-1-118-46070-2 / 1-118-46070-7 456 pp. Pub: 28/04/14 Clinical Trials
978-1-118-57090-6 / 1-118-57090-1 264 pp. Pub: 12/05/14 Clinical Trials
In this book the authors describe how missing data can affect the outcome and credibility of a clinical trial, show by examples how a clinical team can work to prevent missing data, and present the reader with approaches to address missing data effectively.
Choosing Your Phase II Trial Design demonstrates how to approach trial design when there are various options available, such as multiple possible outcome measures, and to use the key characteristics provided to make an informed decision regarding which specific trial design to choose. It sets forth specific points for consideration between the statistician and clinician when designing a phase II trial, including issues such as how the treatment works, choice of outcome measure and randomization. Real life examples and case studies featured throughout the book are accompanied by illustrations using a flow diagram, highlighting the choices made for each key point throughout the process.
The book is illustrated throughout with realistic case studies and worked examples, and presents clear and concise guidelines to enable good planning for missing data. The authors show how to handle missing data in a way that is transparent and easy to understand for clinicians, regulators and patients. New developments are presented to improve the choice and implementation of primary and sensitivity analyses for missing data. Many SAS code examples are included -- the reader is given a toolbox for implementing analyses under a variety of assumptions.
• Demonstrates how to approach trial design when there are various options available, such as multiple possible outcome measures, and to use the key characteristics provided to make an informed decision regarding which specific trial design to choose.
• Presents clear and concise guidelines to enable good planning for missing data. • Illustrated throughout with real--life case studies and worked examples from clinical trials.
• Examines the types of discussions held between the statistician and clinician in order to extract the relevant information required for trial design.
• Includes advice about planning to prevent missing data, and designs to mitigate dropout and to improve ability to correctly impute data.
• Provides an introduction to key concepts for beginners and explores lesser known and alternative approaches to trial design for the more experienced researcher.
• Includes examples worked with R and SAS code. • Explores new developments in the area of sensitivity analyses for missing data.
• Real life examples and case studies feature throughout that use the thought process and corresponding library of designs to practically design phase II trials.
• Authored by an experienced statistician with over 20 years' experience working in the pharmaceutical industry.
• The examples are accompanied by illustrations using a flow diagram, highlighting the choices made for each key point throughout the process.
Statisticians, biostatisticians and researchers involved in clinical trials and in areas such as biopharmaceutical industry, medical and public health organisations. Academics and students needing an introduction to handling missing data, with emphasis on clinical trials.
Statistical researchers involved in designing clinical trials as well as pharmaceutical statisticians and practitioners less familiar with phase II trial design.
Michael O'Kelly, Senior Strategic Biostatistics Director, Centre for Statistics in Drug Development, Innovation, Quintiles Ireland Ltd, Ireland, UK. Bohdana Ratitch, Senior Biostatistican, Quintiles.
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Clinical researchers and medical students involved in trial design as well as postgraduate students on medical statistics courses.
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Statistical Modelling of ICU Applied Missing Data Data Analysis in the Health Sciences Dr Sylvie Chevret, Dr Matthieu RescheRigon, Romain Pirracchio
Xiao-Hua Zhou, Chuan Zhou, Danping Lui, Xaiobo Ding
978-1-119-97926-5 / 1-119-97926-9 288 pp. Pub: 22/09/14 Applied Probability & Statistics - Survival Analysis
978-0-470-52381-0 / 0-470-52381-6 272 pp. Pub: 18/08/14 Biostatistics
Presents both basic and advanced methods on statistical modeling of ICU data.
This book provides a modern, hands-on guide to the essential concepts and ideas for analyzing data with missing observations in the field of biostatistics.
• Provides both an introduction to and an advanced understanding of statistical modeling of ICU data.
• The most current and active areas of missing data research are discussed, including casual inferences for randomized trials with non-compliance, and missing data in diagnostic tests.
• Addresses clinical endpoints used in ICU, such as different measures of mortality and the analysis of repeated measures. • Deals with data from randomized clinical trials and observational nonrandomized studies, often used in ICU.
• Chapters are organized by types of data, allowing readers to work with the book according to their specific research or academic needs.
• Illustrated throughout by case studies and worked examples.
• Examples and data sets can be easily replicated using the SAS®, Stata®, R, and WinBUGS software packages.
Practitioners, statisticians and clinicians involved in the analysis of ICU data. Graduate Students and clinicians involved in the analysis of ICU data.
• Rather than theory, emphasis is placed on hands-on application of various methods, including Bayesian, likelihood, and multiple imputation.
Sylvie Chevret, Matthieu Resche-Rigon and Romain Pirracchio, Department of Biostatistique et Informatique Medicale, Hopital Saint-Louis, Paris, France.
• The authors present case study examples in most chapters to illustrate the real-world use of the discussed methods. As a text for courses on biostatistics at the upperundergraduate and graduate levels; as a resource for health science researchers and applied statisticians; and academic libraries. Xiao-Hua (Andrew) Zhou, PhD, is Professor in the Department of Biostatistics at the University of Washington and Director and Research Career Scientist at the Biostatistics Unit of the Veterans Affairs Puget Sound Health Care System. Dr. Zhou is Associate Editor of Statistics in Medicine and has published over 150 journal articles in his areas of research interest. He is also Fellow is the American Statistical Association and the Royal Statistical Society. Chuan Zhou, PhD, is Assistant Professor of Biostatistics in the School of Medicine at Vanderbilt University. Danping Lui, PhD, is Investigator in the Division of Epidemiology, Statistics and Prevention Research at the national Institute of Health. Xaiobo Ding, PhD, is Postdoctral Fellow at the University of Washington and Assistant Professor in the Academy of Mathematics and Systems Science at the Chinese Academy of Sciences.
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Exploration and Analysis of DNA Microarray and Other High-Dimensional Data, 2e
Design and Analysis of Clinical Trials: Concepts and Methodologies, 3e
Dhammika Amaratunga, Javier Cabrera, Ziv Shkedy
978-0-470-88765-3 / 0-470-88765-6 892 pp. Pub: 16/12/13 Clinical Trials
Shein-Chung Chow, Jen-Pei Liu
978-1-118-35633-3 / 1-118-35633-0 344 pp. Pub: 10/03/14 Statistical Genetics / Microarray Analysis
This new edition provides complete, comprehensive, and expanded coverage of recent health treatments and interventions. Featuring a unified presentation, the book provides a well-balanced summary of current regulatory requirements and recently developed statistical methods as well as an overview of the various designs and analyses that are utilized at different stages of clinical research and development. Additional features include:
The new edition answers the need for an efficient outline of all phases of this revolutionary analytical technique, from preprocessing to the analysis stage. Utilizing research and experience from highly-qualified authors in fields of data analysis, Exploration and Analysis of DNA Microarray and Other High-Dimensional Data, Second Edition features:
• New chapters on biomarker development and target clinical trials, adaptive design, trials for evaluating diagnostic devices, statistical methods for translational medicine, and traditional Chinese medicine.
• A new chapter on the interpretation of findings that includes a discussion of signatures and material on gene set analysis, including network analysis.
• A balanced overview of current and emerging clinical issues as well as newly developed statistical methodologies.
• New topics of coverage including ABC clustering, biclustering, partial least squares, penalized methods, ensemble methods, and enriched ensemble methods.
• Practical examples of clinical trials that demonstrate everyday applicability, with illustrations and examples to explain key concepts.
• Updated exercises to deepen knowledge of the presented material and provide readers with resources for further study.
• New sections on bridging studies and global trials, QT studies, multinational trials, comparative effectiveness trials, and the analysis of QT/QTc prolongation.
As a reference for scientists in biomedical and genomics research fields who have a need to analyze DNA microarray and protein array data; statisticians and bioinformatics practitioners who are involved or interested in the analysis of such data; as a book for courses on statistics, computational biology, and bioinformatics at the graduate level.
• A complete and balanced presentation of clinical and scientific issues, statistical concepts, and methodologies for bridging clinical and statistical disciplines. • An update of each chapter that reflects changes in regulatory requirements for the drug review and approval process and recent developments in statistical design and methodology for clinical research and development
Dhammika Amaratunga, PhD, is Senior Research Fellow in the Nonclinical Biostatistics Department at Johnson & Johnson Pharmaceutical Research & Development, LLC. Javier Cabrera, PhD,m is Full Professor in the Department of Statistics at Rutgers University. Ziv Shkedy, PhD is Associate Professor and Statistical Consultant in the Interuniversity Institute for Biostatistics and Statistical Bioinformatics, Center for Statistics at Hasselt University, Belgium.
Intended as a personal reference work for research and applied/industrial statisticians and biostatisticians; as a course book in clinical trials for students at the first- and second-year graduate levels. Shein-Chung Chow, PhD, is Professor in the Department of Biostatistics and Bioinformatics at Duke University. JenPei Liu, PhD, is an Investigator for the National Health Research Institutes in Taipei, Taiwan. Both authors have extensive background experience in industry and academia, and, collectively, they have published well over twenty books in their respective fields of study. Design and Analysis of Clinical Trials: Concepts and Methodologies, Second Edition – Licensed: Simplified Chinese.
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Differential Equation Analysis in Biomedical Science and Engineering: Partial Differential Equation Applications with R
Differential Equation Analysis in Biomedical Science and Engineering: Ordinary Differential Equation Applications with R
William E. Schiesser
William E. Schiesser
978-1-118-70518-6 / 1-118-70518-1 320 pp. Pub: 21/04/14 Differential Equations
978-1-118-70548-3 / 1-118-70548-3 440 pp. Pub: 31/03/14 Differential Equations
Cataloging much-needed mathematical and computational tools, Differential Equation Analysis in Biomedical Science and Engineering Partial Differential Equation Applications with R provides a solid foundation in formulating and solving real-world PDE numerical and analytical problems in various fields, from applied mathematics, engineering, and computer science to biology and medicine. Addressing the fact that the details of the numerical algorithms and how the solution was computed are usually missing, the text includes supporting documentation and step-by-step guidance, and features R codes that can be easily and conveniently used by students, researchers, and scientists.
With the needed mathematical and computational tools, this book provides a solid foundation in formulating and solving real-world ODE numerical and analytical problems in various fields from applied mathematics, engineering, and computer science to biology and medicine, includes supporting documentation and step-by-step guidance, and features R codes that can be easily and conveniently used by readers. • Provides the routines for programming for ordinary differential equations (ODEs) and numerical algorithms and explains each section of code, including referrals to the mathematical model and algorithms. This approach provides readers with the needed computational details for producing a numerical solution in one convenient place.
• Provides the routines for programming ordinary differential equations (PDEs) and numerical algorithms and explains each section of code, including referrals to the mathematical model and algorithms. This approach provides readers with the needed computational details for producing a numerical solution in one convenient place.
• Presents models as a system of ODEs with explanations of the associated chemistry, physics, biology, and physiology and also includes the initial and boundary conditions and the algebraic equations used to calculate intermediate variables.
• Presents models as a system of PDEs with explanations of the associated chemistry, physics, biology, and physiology and also includes the initial and boundary conditions and the algebraic equations used to calculate intermediate variables.
• Features the use of R software and facilitates immediate use of the computer to solve differential equation problems without having to first learn the basic concepts of numerical analysis for ODEs and the programming of ODE algorithms. All of the R routines are freely available via the author's related website.
• Features the use of R software and facilitates immediate use of the computer to solve differential equation problems without having to first learn the basic concepts of numerical analysis for (PDEs) and the programming of PDE algorithms. All of the R routines are freely available via the author's related website.
• Includes several aspects of general mathematical computation through various bimolecular science and engineering (BMSE) applications, such as solutions for initial value ODEs and boundary value ODEs. Researchers and students.
Researchers and students.
William E. Schiesser, PhD, is R. L. McCann Professor of Chemical Engineering and Professor of Mathematics at Lehigh University. He is also currently Visiting Professor in the Department of Electrical and Systems Engineering at the University of Pennsylvania.
William E. Schiesser, PhD, is R. L. McCann Professor of Chemical Engineering and Professor of Mathematics at Lehigh University. He is also currently Visiting Professor in the Department of Electrical and Systems Engineering at the University of Pennsylvania. S p r i n g / S u m m e r
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Explorations of Mathematical Models in Biology with MATLAB
Spatial and Temporal Dynamics of Infectious Diseases
Dongmei Chen, Bernard Moulin, Jianhong Wu
978-1-118-03212-1 / 1-118-03212-8 304 pp. Pub: 03/03/14 Computational Biology
978-1-118-62993-2 / 1-118-62993-0 512 pp. Pub: 14/07/14 Medical Statistics & Epidemiology
With an emphasis on MATLAB applications to showcase graphical and numerical techniques, this book investigates and analyzes the behavior of solutions of mathematical models and also features interesting linear and models nonlinear models from diverse disciplines, such as biology, ecology, and environment.
This book features recent research and methodology on the spread of infectious diseases and showcases a broad range of multi-disciplinary and state-of-the-art techniques on geo-visualization, remote sensing, metapopulation modeling, cloud computing, and pattern analysis.
As biology increasingly depends on data, algorithms, and models, it has become necessary to use a computing language, such as the user-friendly MATLAB, to focus more on building and analyzing models as opposed to configuring tedious calculations. Explorations of Mathematical Models in Biology with MATLAB provides an introduction to model creation using MATLAB, followed by the translation, analysis, interpretation, and observation of the models.
Features peer-reviewed chapters written by leading experts, Spatial and Temporal Dynamics of Infectious Diseases sheds new light on recent research and methodology on the spread of infectious diseases. Showcasing a broad range of multi-disciplinary and stateof-the-art techniques on geo-visualization, remote sensing, metapopulation modeling, cloud computing, and pattern analysis, as well as examples of disease spreading dynamics, this vital text for students and professionals provides an overview of mathematical modeling and spatial modeling of infectious diseases along with examples of different mathematical, statistical, spatial modeling, and geo-simulation techniques. • Presents approaches for better use of infectious disease data that has been collected by various sources and analysis/modeling technology. • Provides examples of disease spreading dynamics including West Nile virus, bird flu, Lyme disease, pandemic influenza (H1N1), and schistosomiasis. • Contains discussion of mathematical modeling and statistical analysis of infectious diseases as well as spatial and temporal pattern analysis of disease data • Captures state-of-the-art techniques including Smartphone use in spatio-temporal usage data, cloud computing-enabled cluster detection, and comminicable disease geosimulation based on human mobility.
• Examples of real-world applications, such as population dynamics, genetics, drug administration, interacting species, and the spread of contagious diseases, to showcase the relevancy and wide applicability of abstract mathematical techniques. • Discussion of various mathematical concepts, such as Markov chains, matrix algebra, eigenvalues, eigenvectors, first-order linear difference equations, and nonlinear first-order difference equations. • Coverage of difference equations to model a wide range of real-life discrete time situations in diverse areas as well as discussions on matrices to model linear problems. • Solutions to selected exercises and additional MATLAB codes. Students and practitioners.
Students. Researchers and scientists.
Mazen Shahin, PhD, is Professor in the Department of Mathematical Sciences at Delaware State University. He has extensive background and experience in designing interdisciplinary instructional materials that integrate mathematics and other disciplines such as biology, ecology, and finance.
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Dongmei Chen, PhD, is Associate Professor in the Department of Geography and Director of the Laboratory for Geographic Information and Spatial Analysis at Queen's University. Bernard Moulin, PhD, is Professor in the Department of Computer Science and Software Engineering at Laval University. Jianhong Wu, PhD, is University Distinguished Research Professor in the Department of Mathematics and Statistics and Director of the Center for Disease Modeling at York University. 1 5
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Applied Bayesian Modelling, 2e
How to Design, Analyse and Report Cluster Randomised Trials in Medicine and Health Related Research
Peter Congdon 978-1-119-95151-3 / 1-119-95151-8 448 pp. Pub: 18/08/14 Bayesian Analysis
Michael J. Campbell, Stephen J. Walters
Completely revised and updated edition of an established and popular Bayesian statistics text.
978-1-119-99202-8 / 1-119-99202-8 288 pp. Pub: 16/06/14 Clinical Trials
• Provides an accessible overview of statistical modeling applications from a Bayesian perspective. • Updated to include recent advances in methods and models with coverage of R, WinBUGS and JMP.
A much-needed guide to the design and analysis of cluster randomized trials, How to Design, Analyse and Report Cluster Randomised Trials in Medicine and Health Related Research delivers practical guidance on the design and analysis of cluster randomised trials (cRCTs) in healthcare research. Detailing how to use Stata and SPSS and R for statistical analysis, each analysis technique is carefully explained with mathematics kept to a minimum. Written in a clear, accessible style by experienced statisticians, the text provides a practical approach for applied statisticians and biomedical researchers.
• Features a new and updated collection of worked examples from the health and social sciences. • Includes exercises to test the reader's knowledge, featured at the end of each chapter. • Supported by an accompanying website featuring worked-through code to assist readers in developing their own analyses. • Discusses the role of Markov Chain Monte Carlo methods in computing and estimation.
• Provides a practical approach to appeal to both applied statisticians and biomedical researchers.
Researchers in applied statistics, medical science, public health and the social sciences. Graduate students of applied statistics, data analysis and Bayesian methods.
• Each analysis technique is carefully explained with mathematics kept to a minimum.
Peter Congdon is Research Professor of Quantitative Geography and Health Statistics at Queen Mary University of London. He has written three earlier books on Bayesian modelling and data analysis techniques with Wiley, and has a wide range of publications in statistical methodology and in application areas. His current interests include applications to spatial and survey data relating to health status and health service research.
• Explains how to use Stata, and SPSS and R for statistical analysis. • Features real examples, taken from the author's combined 50 years' experience in designing and analysing clinical trials. • Explores advanced topics, such as marginal and random effect generalised linear models with simple explanations and examples provided. • Written in a clear, accessible style by experienced statisticians with a track record of successful publishing. Applied statisticians and quantitative researchers working in the biopharmaceutical industry, academia/higher education, medical and public health organisations. Post graduates working in biomedical statistics; public health/epidemiology, medical and health sciences students. Michael J. Campbell and Stephen J. Walters, School of Health and Related Research, University of Sheffield, UK.
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Statistics for Engineering
Statistical Robust Design: An Industrial Perspective
Modern Industrial Statistics: with applications in R, MINITAB and JMP, 2e
Magnus Arner 978-1-118-62503-3 / 1-118-62503-X 240 pp. Pub: 12/05/14 Engineering Statistics A practical approach to robust design from a statistical and engineering perspective.
Ron Kenett, Shelemyahu Zacks, Daniele Amberti
• Presents a practical approach to robust design whilst also exploring Taguchi methods.
978-1-118-45606-4 / 1-118-45606-8 592 pp. Pub: 03/03/14 Engineering Statistics
• Provides useful concepts and methods for practitioners of robust design, explained in non-mathematical language. • Written from an industrial practitioners perspective, presenting a best practice approach.
The most practical, comprehensive, and fully computerintegrated industrial engineering and quality control text available today.
• Features examples from an industrial setting explaining how to make use of statistics to identify robust design solutions.
Fully revised and updated, this book combines a theoretical background with examples and references to R, MINITAB and JMP, enabling practitioners to find state-ofthe-art material on both foundation and implementation tools to support their work. Topics addressed include computer-intensive data analysis, acceptance sampling, univariate and multivariate statistical process control, design of experiments, quality by design, and reliability using classical and Bayesian methods. The book can be used for workshops or courses on acceptance sampling, statistical process control, design of experiments, and reliability. • Combines a practical approach with theoretical foundations and computational support. • Provides examples in R using a dedicated package called MISTAT, and also refers to MINITAB and JMP. • Includes exercises at the end of each chapter to aid learning and test knowledge. • Provides over 40 data sets representing real-life case studies and is complemented by a comprehensive website. • Presents a comprehensive statistical toolset employing R, MINITAB and JMP for use in business and industry, covering both introductory and advanced methods of quality, reliability and design.
• Includes numerous case studies drawn from the author's own experience in industry. • Supported by an accompanying website hosting solutions to exercises and featuring Matlab. Practitioners of robust industry design as well as consultants providing training in design for Six Sigma and quality engineers as well as university teachings in statistics and quality engineering. Magnus Arner, Global Expert Advisor, Engineering Statistics, Tetra Pak Packaging Solutions. Magnus's area of expertise is in robust design, statistical analysis of reliability and lifetime data and measurement uncertainty, Sweden
Graduate/ post-graduate students in the area of statistical quality and engineering. Industrial statisticians, researchers and practitioners in the areas of quality management, reliability and engineering as well as Black Belts and Master Black Belts. Ron S. Kenett, Chairman and CEO of KPA Ltd.. Shelemyahu Zacks, Binghamton University, Binghamton, USA. M a t h s
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Uncertainty in Risk Assessment: The Representation and Treatment of Uncertainties by Probabilistic and NonProbabilistic Methods
Reliability of Safety-Critical Systems: Theory and Applications Marvin Rausand 978-1-118-11272-4 / 1-118-11272-5 466 pp. Pub: 17/02/14 Quality, Productivity & Reliability
Terje Aven, Enrico Zio, Piero Baraldi, Roger Flage
Reliability of Safety-Critical Systems: Theory and Applications provides a comprehensive introduction to reliability assessments of safety-related systems based on electrical, electronic, and programmable electronic (E/E/PE) technology. With a focus on the design and development phases of safety-critical systems, the book presents theory and methods required to document compliance with IEC 61508 and the associated sector-specific standards.
978-1-118-48958-1 / 1-118-48958-6 200 pp. Pub: 10/03/14 Engineering Statistics In providing guidance for practical decision-making situations concerning high-consequence technologies (e.g., nuclear, oil and gas, transport, etc.), the theories and methods studied in Uncertainty in Risk Assessment have wide-ranging applications from engineering and medicine to environmental impacts and natural disasters, security, and financial risk management. The main focus, however, is on engineering applications. • Illustrates the need for seeing beyond probability to represent uncertainties in risk assessment contexts. • Provides simple explanations (supported by straightforward numerical examples) of the meaning of different types of probabilities, including interval probabilities, and the fundamentals of possibility theory and evidence theory. • Offers guidance on when to use probability and when to use an alternative representation of uncertainty. • Presents and discusses methods for the representation and characterization of uncertainty in risk assessment. • Addresses an important and current problem for which there are competing solutions with an emphasis on and discussion of practical applications. • Provides foundations, background references and practical guidelines to uncertainty theories that explicitly model imprecision and encompass both interval and probabilistic approaches.
Combining theory and practical applications, Reliability of Safety-Critical Systems: Theory and Applications implements key safety-related strategies and methods to meet quantitative safety integrity requirements. In addition, the book details a variety of reliability analysis methods that are needed during all stages of a safety-critical system, beginning with specification and design and advancing to operations, maintenance, and modification control. The key categories of safety life-cycle phases are featured, including strategies for the allocation of reliability performance requirements; assessment methods in relation to design; and reliability quantification in relation to operation and maintenance. Issues and benefits that arise from complex modern technology developments are featured, as well as: • Real-world examples from large industry facilities with major accident potential and products owned by the general public such as cars and tools. • Plentiful worked examples throughout that provide readers with a deeper understanding of the core concepts and aid in the analysis and solution of common issues when assessing all facets of safety-critical systems. • Approaches that work on a wide scope of applications and can be applied to the analysis of any safety-critical system. • A brief appendix of probability theory for reference.
Professionals and researchers. Graduate students.
Industrial professionals, consultants, and operators.
Terje Aven, Professor of Risk Analysis & Risk Management, University of Stavanger. Enrico Zio, Director of the Chair in Complex Systems and the Energetic Challenge, Ecole Centrale Paris and Supelec, and Professor of Reliability, Safety & Risk Analysis, Politecnico di Milano. Piero Baraldi, Assistant Professor of Nuclear Engineering, Department of Energy, Politecnico di Milano. Roger Flage, Adjunct Assistant Professor, University of Stavanger S p r i n g / S u m m e r
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Marvin Rausand, PhD, is Professor in the Department of Production and Quality Engineering at Norwegian University of Science and Technology and has over thirty years of academic experience and has published more than fifty journal articles. Dr. Rausand is the author of System Reliability Theory: Models, Statistical Methods, and Applications, Second Edition, published by Wiley. 1 8
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Solutions Manual to Accompany Statistics and Probability with Applications for Engineers and Scientists, Solutions Manual
Environmental Statistics Statistical Applications for Environmental Analysis and Risk Assessment Joseph Ofungwu 978-1-118-63453-0 / 1-118-63453-5 656 pp. Pub: 16/06/14 Environmental Statistics & Environmetrics
Bhisham C. Gupta, Irwin Guttman 978-1-118-78969-8 / 1-118-78969-5 376 pp. Pub: 16/12/13 Engineering Statistics
This book stresses and explains the importance -- and unawareness -- of a basic knowledge of statistics and statistical analysis in the environmental sciences.
Unique among books of this kind, Statistics and Probability with Applications for Engineers and Scientists covers descriptive statistics first, then goes on to discuss the fundamentals of probability theory. Along with case studies, examples, and real-world data sets, the book incorporates clear instructions on how to use the statistical packages Minitab® and Microsoft® Office Excel® to analyze various data sets. The book also features: • Detailed discussions on sampling distributions, statistical estimation of population parameters, hypothesis testing, reliability theory, statistical quality control including Phase I and Phase II control charts, and process capability indices. • A clear presentation of nonparametric methods and simple and multiple linear regression methods, as well as a brief discussion on logistic regression method. • Comprehensive guidance on the design of experiments, including randomized block designs, one- and two-way layout designs, Latin square designs, random effects and mixed effects models, factorial and fractional factorial designs, and response surface methodology. • A companion website containing data sets for Minitab and Microsoft Office Excel, as well as JMP ® routines and results.
• This is a general-purpose environmental statistics book that does not assume prior familiarity with the subject. The emphasis is on applications in such areas as hydrology, hydrogeology, contaminant hydrogeology, and water and air quality. • Chapters are self-contained to allow for flexibility of choice. • The book's major theme is to describe statistical applications for conventional deterministic as well as probabilistic risk assessment. • Unlike many competitors, regression is covered in some detail. • Cognizant of the reality that expensive statistical software is not always available or that learning curves to digest freely-available packages is limited, the example applications include a variety of software programs ranging from Minitab and Excel 2010 to R. • An author-maintained web site is available to further explore data sets that are presented in the book. Statistical Applications for Environmental Analysis and Risk Assessment is an excellent book for courses on quantitative methods in applied statistics, geology, geography, natural resources, and environmental sciences at the upperundergraduate and graduate levels. It is also a valuable reference for earth scientists, geologists, hydrologists, and environmental statisticians who collect and analyze data in their everyday work.
Assuming no background in probability and statistics, Statistics and Probability with Applications for Engineers and Scientists features a unique, yet tried-and-true, approach that is ideal for all undergraduate students as well as statistical practitioners who analyze and illustrate real-world data in engineering and the natural sciences.
Joseph Ofungwu, PhD, is an environmental professional with 17 years of hands-on experience in the environmental and water resources practice areas at the US Army Corps of Engineers and the New Jersey Department of Transportation. He is currently a principal engineer at The Louis Berger Group, Inc. He has published several peerreviewed papers on health risk assessment methodologies for unusual exposure scenarios.
Bhisham C. Gupta, PhD, is Professor and Chair in the Department of Mathematics and Statistics at the University of Southern Maine. Irwin Guttman, PhD, is retired Professor of Statistics in the Department of Mathematics at the State University of New York at Buffalo and a Fellow of the American Statistical Association, the Institute of Mathematical Statistics, the International Statistical Institute, and the Royal Statistical Society. M a t h s
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Understanding Large Temporal Networks and Spatial Networks: Exploration, Pattern Searching, Visualization and Network Evolution
Statistical Hypothesis Testing with SAS and R Dirk Taeger, Sonja Kuhnt 978-1-119-95021-9 / 1-119-95021-X 312 pp. Pub: 21/04/14 Computational & Graphical Statistics
Patrick Doreian, Vladimir Batagelj, Anuska Ferligoj, Natasa Kejzar
A comprehensive guide to Statistical Hypothesis Testing with examples in SAS and R.
978-0-470-71452-2 / 0-470-71452-2 416 pp. Pub: 14/07/14 Biometrics
This book provides a reference guide to statistical tests and their application to data using SAS and R. A general summary of statistical test theory is presented, along with a general description for each test, together with necessary prerequisites, assumptions, and the formal test problem. The test statistic is stated together with annotations on its distribution, along with examples in both SAS and R. Each example contains the code to perform the test, the output, and remarks that explain necessary program parameters.
A comprehensive and over-reaching work by an excellent team of authors at the forefront of this hot topic. This book explores social mechanisms that drive network change and link them to computationally sound models of changing structure to detect patterns. This text identifies the social processes generating these networks and how networks have evolved.
• Presents a comprehensive guide to hypothesis testing using SAS and R.
• Authors are leaders in this evolving subject.
• Covers statistical test theory, providing a general description for each test.
• Provides an overview of different approaches to studying large temporal and spatial networks.
• Provides examples in both SAS and R for each test presented.
• An existing popular website is available for data download.
• Looks at the most common statistical tests, providing guidance on performing the correct statistical test.
• Contains multiple examples of networks. • Examines evolutionary networks generative and network mechanisms.
• Supported by a supplementary website http://www.dtaeger.de featuring example program code.
• Supported by downloadable datasets via website link.
Academics and practitioners, SAS and R programmers who want to conduct statistical tests.
• Features bibliometric dynamics and the structure of science.
Students using SAS and R.
Scientists. Researchers and Postgraduate Students.
Dirk Taeger, Center of competence of Epidemiology of the Institute for Prevention and Occupational Medicine of the German Social Accident Insurance Institute of the RuhrUniversitat Bochum (IPA). Germany. Sonja Kuhnt, Institute for Mathematical Statistics with Application in Industry.
Patrick Doreian is Professor Emeritus from the University of Pittsburgh. He has published over 100 articles in academic journals and book chapters. His co-authored book Generalized Blockmodeling received the Harrison White Outstanding Book Award in 2007. Anuska Ferligoj is Professor of Statistics at the Faculty of Social Sciences, University of Ljubljana. Natasa Kejzar is Teaching Assistant of Informatics at the University of Ljubljana, Faculty of Social Sciences. Vladimir Batagelj is Professor of discrete and computational mathematics at the University of Ljubljana and is chair of the Department of Theoretical Computer Science at IMFM, Ljubljana. He is a member of the editorial boards of Informatica and the Journal of Social Structure.
Faculty of Statistics, TU Dortmund University, Germany.
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Nonparametric Hypotheses Online Panel Research: A Testing with R - Rank: Data Quality Perspective Tests and Permutation Mario Callegaro, Reginald P. Baker, Jelke Bethlehem, Anja S. Goritz, Jon A. Tests Krosnick, Paul J. Lavrakas
Stefano Bonnini, Luigi Salmosa, Marco Marozzi, Livio Corain
978-1-119-94177-4 / 1-119-94177-6 520 pp. Pub: 14/07/14 Survey Research Methods & Sampling
978-1-119-95237-4 / 1-119-95237-9 256 pp. Pub: 01/09/14 Computational & Graphical Statistics Presents a comprehensive guide to methods for hypothesis testing with R.
This book explores the methodology behind online panels and addresses what should and should not be claimed about the validity of data that is generated by this rapidly growing mode of research.
• Provides a brief history of online panels and methods that have been used.
• Presents a comprehensive guide to nonparametric methods for hypothesis testing with R.
• Explores sampling and coverage error, along with probability and nonprobability methods.
• Covers both established and newly developed methods such as testing problems with missing data, comparison of two treatments for independent samples and comparison of two treatments for paired data.
• Looks at specific survey sampling, screening and data collection issues. • Addresses panel maintenance and attrition, along with online panel recruitment.
• Looks at statistical problems such as tests for repeated measures, multivariate and multistrata tests and tests for categorical variables. • Features case studies and exercises for each problem.
• Presents contributions from the world's leading experts in the field.
• Supported by a supplementary website featuring R code and instructions for users.
• Supported by a supplementary website featuring data sets.
Advanced level students dealing with non-parametric problems in science and engineering.
• Presents current online panel sampling methods. Survey and marketing researchers, including governmentbased survey statisticians and methodologists. Social scientists in political science, sociology, and psychology who use survey data.
Consultant experts in statistics. Stefano Bonnini, Assistant Professor of Statistics, Faculty of Economics, Department of Economics, University of Ferrara, Italy. Livio Corain, Assistant Professor of Statistics, Faculty of Engineering, Department of Management and Engineering, University of Padova, Italy. Marco Marozzi, Associate Professor of Statistics, Faculty of Economics, Department of Economics and Statistics, University of Calabria, Italy. Luigi Salmaso, Full Professor of Statistics, Faculty of Engineering, University of Padova, Italy.
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Mario Callegaro, Survey Research Scientist, Quantitative Marketing Google Inc., UK. Reg Baker, President & Chief Operating Officer, Market Strategies International, USA. Paul J. Lavrakas, Nielsen Media Research, Research Psychologist/Research Methodologist, USA. Jon A. Krosnick, Professor of Political Science, Communication, Psychology, Stanford University, USA. Jelke Bethlehem, Department of Quantitative Economics, University of Amsterdam,The Netherlands. Anja Goritz, University of Erlangen-Nuremberg, Department of Economics and Social Psychology, Germany.
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Propagation Dynamics on Complex Networks: Models, Methods and Stability Analysis
Statistical Analysis in Forensic Science: Evidential Values of Multivariate Physicochemical Data
Xinchu Fu, Michael Small, Guanrong Chen
Grzegorz Zadora, Agnieszka Martyna, Daniel Ramos, Colin G. Aitken
978-1-118-53450-2 / 1-118-53450-6 328 pp. Pub: 10/03/14 Applied Probability & Statistics
978-0-470-97210-6 / 0-470-97210-6 336 pp. Pub: 03/03/14 Applied Probability & Statistics
Explores emerging topics of epidemic dynamics on complex networks, including theories, methods, and realworld applications with elementary and wide-coverage, suitable to use as a textbook, as well as a research reference book.
Microtraces of various materials (e.g. glass, paint, fibres, and petroleum products) are routinely subjected to physicochemical examination by forensic experts, whose role is to evaluate such physicochemical data in the context of the prosecution and defence propositions. Such examinations return various kinds of information, including quantitative data. From the forensic point of view, the most suitable way to evaluate evidence is the likelihood ratio. This book provides a collection of recent approaches to the determination of likelihood ratios and describes suitable software, with documentation and examples of their use in practice. The statistical computing and graphics software environment R, pre-computed Bayesian networks using Hugin Researcher and a new package, calcuLatoR, for the computation of likelihood ratios are all explored.
Throughout history epidemic diseases have presented a serious threat to human life, and in recent years the spread of infectious diseases such as dengue, malaria, HIV, and SARS has captured global attention; and in the modern technological age, the proliferation of virus attacks on the Internet highlights the emergent need for knowledge about modeling, analysis, and control in epidemic dynamics on complex networks. For advancement of techniques, it has become clear that more fundamental knowledge will be needed in mathematical and numerical context about how epidemic dynamical networks can be modelled, analyzed, and controlled. This book explores recent progress in these topics and looks at issues relating to various epidemic systems.
• Description of the physicochemical analysis of forensic trace evidence and detailed description of likelihood ratio models for determining the evidential value of multivariate physicochemical data.
• Includes a brief history of mathematical epidemiology and epidemic modeling on complex networks.
• Detailed description of methods, such as empirical crossentropy plots, for assessing the performance of likelihood ratio-based methods for evidence evaluation.
• Explores how information, opinion, and rumor spread via the Internet and social networks.
• Explores the aims of evaluating physicochemical data for forensic purposes, with an emphasis on comparison and classification tasks.
• Presents plausible models for propagation of SARS and avian influenza outbreaks, providing a reality check for otherwise abstract mathematical modeling.
• Looks at continuous data, evidence evaluation using likelihood.
• Considers various infectivity functions, including constant, piecewise-linear, saturated, and nonlinear cases.
• Ratios and ratio models for comparison problem and classification problems.
Researchers. Graduate Students.
• Supported by website featuring appendices and R datasets.
Xinchu Fu, Professor, Shanghai University, Shanghai, China. Michael Small, Professor, University of Western Australia, Australia. Guanrong Chen, Chair Professor, City University of Hong Kong, Hong Kong SAR, China.
Forensic Experts and Practitioners, Forensic Statisticians, Analytical Chemists and Chemometricians. Grzegorz Zadora, Institute of Forensic Research, Krakow, Poland. Daniel Ramos, Telecommunication Engineering, Universidad Autonoma de Madrid, Spain.
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Design, Evaluation, and Multivariate Nonparametric Analysis of Questionnaires Regression and for Survey Research, 2e Visualization: With R and Applications to Finance Willem E. Saris, Irmtraud N. Gallhofer Jussi Klemela
978-1-118-63461-5 / 1-118-63461-6 376 pp. Pub: 05/05/14 Survey Research Methods & Sampling
978-0-470-38442-8 / 0-470-38442-5 396 pp. Pub: 21/04/14 Statistical Software / R
This book explores updates on the statistical knowledge and development of survey questionnaires, including analyzing the important decisions researchers make throughout the survey design process. The new edition provides coverage of an updated SQP program, which has an expanded question database from the Multi-trait Multimethod (MTMM) experiments. This book aims to give students and survey researchers a state-of-the-art introduction to questionnaire design and how to construct questionnaires with the highest relevance and accuracy. The pitfalls of questionnaire design are outlined throughout the book, which alerts designers of questionnaires to the many prior decisions that will affect the quality of the research outcome. It is important to measure the quality of questions at the outset in order for students and researchers to consider the consequences and methods of achieving reliable and effective questions.
This cutting-edge book is the first of its kind to use a visualization approach to explore the key concepts of statistical learning. With a unique and innovative presentation, Multivariate Nonparametric Regression and Visualization provides readers with the core statistical concepts to obtain complete and accurate predictions when given a set of data. Focusing on nonparametric methods to adapt to the multiple types of data generating mechanisms, the book begins with an overview of classification and regression. The book then introduces and examines various tested and proven visualization techniques for learning samples and functions. The book identifies risk management, portfolio selection, and option pricing as the main areas in which statistical methods may be implemented in quantitative finance. The book provides coverage of key statistical areas including linear methods, kernel methods, additive models and trees, boosting, support vector machines, and nearest neighbor methods. Exploring the additional applications of nonparametric and semiparametric methods, Multivariate Nonparametric Regression and Visualization features:
• An introduction to the updated version of the computer program SQP (SQP 2.0), with an extended database of more than 3000 survey questions to over twenty-two countries and in most European languages. • An explanation of the usage and limitations of SPQ 2.0 in a classroom to test quality of questions without having to collect data.
• An extensive appendix with R-package training material to encourage duplication and modification of the presented computations and research.
• Outlined decision making steps to help students and researchers consider the consequences of survey questionnaires.
• Multiple examples to demonstrate the applications in the field of finance.
• Practical, contemporary examples illustrate the many pitfalls of questionnaire design.
• Sections with formal definitions of the various applied methods for readers to utilise throughout the book.
Under/graduates. Scientists. Reseachers. Willem E. Sarias PhD, is Professor in Methodology at the Universitat Pompeu Fabra, Barcelona and ESADE, Barcelona. He is Laureate of the 2005 Descartes Prize for "Best Collaborative Research" and has published well over 200 articles during his tenure as an academician and is an active member of over a dozen regional and international societies. Irmtraud Gallhofer PhD, is Senior Researcher on projects of the European Social Survey (ESS), RECSM at the Universitat Pompeu Fabra, Barcelona. She has been invited to speak at over two-dozen international universities during tenure as an academician.
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As a text for courses on nonparametric function estimation or advanced special topics in statistics; As a reference for practitioners in the field of finance. Jussi Klemela, PhD, is a Professor and Researcher in the Department of Mathematical Sciences at the University of Oulu in Finland. He has authored or co-authored numerous journal articles and has given talks worldwide on density estimation and the implementation of advanced visualization tools.
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Applied Linear Regression, Fast Sequential Monte 4e Carlo Methods for Counting and Optimization Sanford Weisberg Reuven Y. Rubinstein, Ad Ridder, Radislav Vaisman
978-1-118-38608-8 / 1-118-38608-6 368 pp. Pub: 20/01/14 Regression Analysis
978-1-118-61226-2 / 1-118-61226-4 208 pp. Pub: 06/01/14 Applied Probability & Statistics
The Fourth Edition of Applied Linear Regression provides a thorough update of the basic theory and methodology of linear regression modeling. Demonstrating the practical applications of linear regression analysis techniques, the Fourth Edition uses interesting, real-world exercises and examples.
This book presents the first comprehensive account of fast sequential Monte Carlo (SMC) methods for counting and optimization at an exceptionally accessible level.
Stressing central concepts such as model building, understanding parameters, assessing fit and reliability, and drawing conclusions, the new edition illustrates how to develop estimation, confidence, and testing procedures primarily through the use of least squares regression. While maintaining the accessible appeal of each previous edition, Applied Linear Regression, Fourth Edition features:
Based on years of research in efficient Monte Carlo methods for estimation of rare-event probabilities, counting problems, and combinatorial optimization, Fast Sequential Monte Carlo Methods for Counting and Optimization is a complete illustration of fast sequential Monte Carlo techniques. The book provides an accessible overview of current work in the field of Monte Carlo methods, specifically sequential Monte Carlo techniques, for solving abstract counting and optimization problems. Written by authorities in the field, the book places emphasis on crossentropy, minimum cross-entropy, splitting, and stochastic enumeration. Focusing on the concepts and application of Monte Carlo techniques, Fast Sequential Monte Carlo Methods for Counting and Optimization includes:
• Graphical methods stressed in the initial exploratory phase, analysis phase, and summarization phase of an analysis. • In-depth coverage of parameter estimates in both simple and complex models, transformations, and regression diagnostics.
• Detailed algorithms problems.
• Newly added material on topics including testing, ANOVA, and variance assumptions.
• A new generic sequential importance sampling algorithm alongside extensive numerical results.
Applied Linear Regression, Fourth Edition is an excellent textbook for upper-undergraduate and graduate-level students, as well as an appropriate reference guide for practitioners and applied statisticians in engineering, business administration, economics, and the social sciences.
• An appendix focused on review material to provide additional background information. For use as a textbook/supplement at upper-undergraduate and/or beginning graduate levels. It can also be used as a source book for practicing statisticians, engineers, computer scientists, or mathematicians and anyone who is interested in efficient simulation and/or efficient combinatorial optimization and counting.
Sanford Weisberg, PhD, is Professor of Statistics and Director of the Statistical Consulting Service at the University of Minnesota. He has authored or coauthored three popular texts for John Wiley & Sons, Inc. and is a Fellow of the American Statistical Association.
Reuven Y. Rubinstein, DSc, is Professor Emeritus in the Faculty of Industrial Engineering and Management at Technion-Israel Institute of Technology. He has served as a consultant at numerous large-scale organizations, such as IBM, Motorola, and NEC. Adrianus A. N. Ridder, PhD, is Associate Professor of Economics and Operations Research at Vrije Universiteit Amsterdam. Radislav Vaisman, PhD, is a senior software engineer at Tech-Mer in Israel.
Applied Linear Regression, 2nd Edition – Licensed: Orthodox Chinese, Simplified Chinese.
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• Numerous examples with Monte Carlo method produced solutions within the 1-2% limit of relative error.
• Updated methodology, such as bootstrapping, crossvalidation binomial and Poisson regression, and modern model selection methods.
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Growth Curve Modeling: Theory and Applications
Nonparametric Statistical Methods, 3e
Michael J. Panik
Myles Hollander, Douglas A. Wolfe, Eric Chicken
978-1-118-76404-6 / 1-118-76404-8 454 pp. Pub: 03/02/14 Longitudinal Analysis
978-0-470-38737-5 / 0-470-38737-8 848 pp. Pub: 30/12/13 Nonparametric Analysis
This book addresses recent trends and advances in growth curve modeling and features practical applications to economic, plant, population, public health, forest, and firm growth, to name a few.
Thoroughly revised and updated, this new edition includes additional modern topics and procedures, more practical data sets, and new problems from real-life situations. The book continues to emphasize the importance of nonparametric methods as a significant branch of modern statistics and equips readers with the conceptual and technical skills necessary to select and apply the appropriate procedures for any given situation.
Growth Curve Modeling: Theory and Applications features an accessible introduction to growth curve modeling and addresses how to monitor the change in variables over time since there is no "one size fits all" approach to growth measurement. A review of the requisite mathematics for growth modeling and the statistical techniques needed for estimating growth models are provided, and an overview of popular growth curves, such as linear, logarithmic, reciprocal, logistic, Gompertz, Weibull, negative exponential, and log-logistic, among others, is included.
It provides readers with crucial nonparametric techniques in a variety of settings, emphasizing the assumptions underlying the methods. The book provides an extensive array of examples that clearly illustrate how to use nonparametric approaches for handling one- or two-sample location and dispersion problems, dichotomous data, and one-way and two-way layout problems. • The use of the freely available R software to aid in computation and simulation, including many new R programs written explicitly for this new edition. • New chapters that address density estimation, wavelets, smoothing, ranked set sampling, and Bayesian nonparametrics. • Problems that illustrate examples from agricultural science, astronomy, biology, criminology, education, engineering, environmental science, geology, home economics, medicine, oceanography, physics, psychology, sociology, and space science.
In addition, the book discusses key application areas including economic, plant, population, forest, and firm growth and is suitable as a resource for assessing recent growth modeling trends in the medical field. SAS® is utilized throughout to analyze and model growth curves, aiding readers in estimating specialized growth rates and curves. • Statistical distribution analysis as it pertains to growth modeling. • Trend estimations. • Dynamic site equations obtained from growth models. • Nonlinear regression. • Yield-density curves. • Nonlinear mixed effects models for repeated measurements data.
As a reference for applied statisticians and practitioners working in industry who seek a review of nonparametric methods and their relevant applications; as a textbook for an upper-level undergraduate or first-year graduate course in applied nonparametric statistics; and academic libraries.
As a resource/reference book for researchers and professionals in statistics, economics, biology, medicine, public health, and pharmaceutical fields who desire coverage of modern statistical methods for modeling growth curves and analyzing longitudinal data; as a textbook for upper-undergraduate and graduate students.
Myles Hollander, PhD, Emeritus and Robert O. Lawton Distinguished Professor, Department of Statistics, Florida State University. He has authored of over 100 journal articles and is a Fellow of the Institute of Mathematical Statistics and the American Statistical Association. Douglas A. Wolfe, PhD, Professor & Chair, Dept of Statistics, The Ohio State University. Eric Chicken, PhD, Assistant Professor, Department of Statistics, Florida State University. A member of the American Statistical Association and the Institute of Mathematical Statistics, Dr. Chicken is the author of ten journal articles.
Michael J. Panik, PhD, is Professor Emeritus in the Department of Economics at the University of Hartford. The author of numerous books and journal articles in the areas of economics, mathematics, and applied econometrics, he is a member of the Operations Research Society of America, the Decision Sciences Institute, the Mathematical Programming Society, the American Economic Association, the Econometric Society, and the Mathematical Association of America. M a t h s
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Nonparametric Statistical Methods, 2nd Edition – Licensed: Russian. 2 5
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Nonparametric Statistics: A Step-by-Step Approach, 2e
Repeated Measurements and Cross-Over Designs
Gregory W. Corder, Dale I. Foreman
978-1-118-70925-2 / 1-118-70925-X 266 pp. Pub: 05/05/14 Experimental Design
Damaraju Raghavarao, Lakshmi Padgett
978-1-118-84031-3 / 1-118-84031-3 282 pp. Pub: 19/05/14 Nonparametric Analysis
This book provides an introduction to state-of-the-art experimental design approaches to better understand and interpret repeated measurement data in cross-over designs.
This new edition presents a practical and understandable approach that enhances and expands the statistical toolset for readers. This book includes: • New coverage of the sign test and the KolmogorovSmirnov two-sample test in an effort to offer a logical and natural progression to statistical power. • SPSS® (Version 21) software and updated screen captures to demonstrate how to perform and recognize the steps in the various procedures. • Data sets and odd-numbered solutions provided in an appendix, and tables of critical values. • Supplementary material to aid in reader comprehension, which includes: narrated videos and screen animations with step-by-step instructions on how to follow the tests using SPSS; online decision trees to help users determine the needed type of statistical test; and additional solutions not found within the book. • Presents a practical and understandable approach that enhances and expands the statistical toolset for readers and has been extensively classroom tested at James Madison University and Shenandoah University. • Recognizes the continuous growth of nonparametric statistical applications and aids future and existing scientists and practitioners in interpreting and applying nonparametric statistics.
• Features the close tie between the design, analysis, and presentation of results. • Presents principles and rules that apply very generally to most areas of research, such as clinical trials, agricultural investigations, industrial procedures, quality control procedures, and epidemiological studies. • Includes many practical examples, such as PK/PD studies in the pharmaceutical industry, k-sample and one sample repeated measurement designs for psychological studies, and residual effects of different treatments in controlling conditions such as asthma, blood pressure, and diabetes. • Utilises SAS(R) software to draw necessary inferences. All SAS output and data sets are available via the book's related website. This book is ideal for a broad audience including statisticians in pre-clinical research, researchers in psychology, sociology, politics, marketing, and engineering. Lakshmi Padgett, PhD, is Senior Manager at Centocor, a biotechnology company with a focus on the development of diagnostic medical tests. The author of approximately 20 journal articles, she received her PhD in Statistics from Temple University and has experience in phase I, II, and III trials.
As a textbook for courses on nonparametric statistics for upper-undergraduate and/or beginning graduate students majoring in the physical, social, behavioral, and health sciences as well as secondary education; as a refresher for professionals and researchers in the social, behavioral, and health sciences; as a reference for applied statisticians and practitioners working in industry who seek a review of nonparametric methods and their relevant applications; as a reference for secondary educators; and academic libraries. Greg W. Corder, PhD, is Adjunct Instructor in the Department of Physics and Astronomy at James Madison University, and he is also Adjunct Instructor in undergraduate and graduate education at Mary Baldwin College. Dale I. Foreman, PhD, is Associate Professor in the Department of Research and Social Analysis at Shenandoah University.
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Statistical Inference for Models with Multivariate tDistributed Errors
Statistics for Finance and Business Business Risk Management: Models and Analysis
A. K. Md. Ehsanes Saleh, Mohammad Arashi, S M M Tabatabaey 978-1-118-85405-1 / 1-118-85405-5 288 pp. Pub: 04/08/14 Multivariate Analysis
Edward J. Anderson 978-1-118-34946-5 / 1-118-34946-6 384 pp. Pub: 03/02/14 Management Science / Operations Research
This book uniquely addresses the use of Student's tdistributed errors in linear models and makes the connection to normal distribution, Bayesian analysis, prediction problems, and Stein shrinkage estimation to aid in reader comprehension.
Provides the foundations needed for managers who have to make decisions in an uncertain and risky environment.
This book summarizes the results of various models under normal theory with a brief review of the literature. Statistical Inference for Models with Multivariate tDistributed Errors:
The business world is rife with risk and uncertainty, and risk management is a vitally important topic for managers. The best way to achieve a clear understanding of risk is to use quantitative tools and probability models. Written for students, this book has a quantitative emphasis but is accessible to those without a strong mathematical background.
• Includes a wide array of applications for the analysis of multivariate observations. • Emphasizes the development of linear statistical models with applications to engineering, the physical sciences, and mathematics.
• Discusses novel modern approaches to risk management. • Introduces advanced topics in an accessible manner.
• Contains an up-to-date bibliography featuring the latest trends and advances in the field to provide a collective source for research on the topic.
• Includes motivating worked examples and exercises (including selected solutions). • Is written with the student in mind, and does not assume advanced mathematics.
• Addresses linear regression models with non-normal errors with practical real-world examples.
• Is suitable for self-study by the manager who wishes to better understand this important field.
• Uniquely addresses regression models in Student's tdistributed errors and t-models.
Aimed at postgraduate students, this book is also suitable for senior undergraduates, MBA students, and all those who have a general interest in business risk. Managers with an interest in applying advanced risk management tools in their business. Academics within Decision Sciences and Operations Research.
• Supplemented with an Instructor's Solutions Manual, which is available via written request by the Publisher. As an upper-undergraduate and graduate-level textbook or supplement for a two-semester course in multivariate analysis, regression, linear models, and/or Bayesian analysis; as a resource for statistical practitioners and academics who need solid methodology within mathematical and/or quantitative statistics; and academic and corporate libraries.
Edward Anderson, University of Sydney Business School, University of Sydney, Australia.
A. K. Md. Ehsanes Saleh, PhD, is Distinguished Research Professor in the School of Mathematics and Statistics at Carleton University. M. Arashi, PhD, is Associate Professor in the Department of Statistics at Shahrood University of Technology in Iran. S. M. M. Tabatabaey, PhD, is Associate Professor in the Department of Statistics at Ferdowski University of Mashhad in Iran.
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Discrete-Event Simulation and System Dynamics for Management Decision Making
Practical Data Mining for Business: Case Studies and Methodology Andrea Ahlemeyer-Stubbe, Shirley Coleman
Sally Brailsford, Leonid Churilov, Brian Dangerfield
978-1-119-97713-1 / 1-119-97713-4 312 pp. Pub: 16/06/14 Data Mining Statistics
978-1-118-34902-1 / 1-118-34902-4 360 pp. Pub: 26/05/14 Operations Research & Management Science (ghost code - use BA11)
A user friendly guide to data mining and its applications. Data mining is well on its way to becoming a recognized discipline in the overlapping areas of IT, statistics, machine learning, and AI. Practical Data Mining for Business presents a user-friendly approach to data mining methods, covering the typical uses to which it is applied. The methodology is complemented by case studies to create a versatile reference book, allowing readers to look for specific methods as well as for specific applications. The book is formatted to allow statisticians, computer scientists, and economists to cross-reference from a particular application or method to sectors of interest.
Explores the integration of discrete-event simulation (DES) and system dynamics (SD), providing comparisons of each methodology. In recent years, there has been a growing debate, particularly in the UK and Europe, over the merits of using discrete-event simulation (DES) and system dynamics (SD); there are now instances where both methodologies were employed on the same problem. This book details each method, comparing each in terms of both theory and their application to various problem situations. It also provides a seamless treatment of various topics--theory, philosophy, detailed mechanics, practical implementation-providing a systematic treatment of the methodologies of DES and SD, which previously have been treated separately.
• Presents data mining processes, methods and commonly used methods for descriptive and exploratory statistics using SAS and JMP. • Explores applications such as marketing, as well as quality management and medicine.
• Explores the integration of DES and SD, providing comparisons of each methodology.
• Looks at how to prepare data to mine, and develop accurate data mining questions.
• Explores the most appropriate methodology and combination of methodologies and how should be applied.
• Supported by a supplementary website featuring datasets and solutions. Statisticians, computer scientists and economists involved with data mining as well as students studying economics, business administration and international marketing. Data Mining Professionals, Business Intelligence Professionals and Database Marketing Professionals.
• Provides a seamless treatment of a variety of topics: theory, philosophy, detailed mechanics and practical implementation of both DES and SD. • Supported by an accompanying website hosting models and software.
Andrea Ahlemeyer-Stubbe, Owner, antz21 GmbH, Gegenbach, Germany. Shirley Coleman, Technical Director, ISRU, School of Maths and Stats, Newcastle University, UK.
Academics and graduate students in business and management, maths/operations research, computer science, and industrial engineering. Practitioners in consulting companies, government departments and analytics professionals in the private sector. Post graduate students and managers who are not necessarily part of the (quantitatively inclined) primary market. Sally Brailsford, School of Management, University of Southampton, UK. Leonid Churilov, Melbourne Brain Centre, Victoria, Australia. Brian Dangerfield, Salford Business School, University of Salford, UK.
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Panel Data Analysis using EViews
Quantile Regression: Theory and Applications
I Gusti Ngurah Agung
Cristina Davino, Marilena Furno, Domenico Vistocco
978-1-118-71558-1 / 1-118-71558-6 544 pp. Pub: 10/03/14 Statistics for Finance, Business & Economics
978-1-119-97528-1 / 1-119-97528-X 276 pp. Pub: 27/01/14 Regression Analysis
This book explores the use of EViews software in creating panel data analysis using appropriate empirical models and real datasets. Guidance is given on developing alternative descriptive statistical summaries for evaluation and providing policy analysis based on pool panel data. Various alternative models based on panel data are explored, including univariate general linear models, fixed effect models and causal models, and guidance on the advantages and disadvantages of each one is given.
A balanced guide to the implementation and interpretation of Quantile Regression models. This book explores the theory and numerous applications of quantile regression, offering empirical data analysis as well as the software tools to implement the methods. The main focus of this book is to provide the reader with a comprehensive description of the main issues concerning quantile regression; these include basic modeling, geometrical interpretation, estimation and inference for quantile regression, as well as issues on validity of the model, diagnostic tools. Each methodological aspect is explored and followed by applications using real data.
• Provides step-by-step guidance on how to apply EViews software to panel data analysis using appropriate empirical models and real datasets. • Examines a variety of panel data models along with the author's own empirical findings, demonstrating the advantages and limitations of each model.
• Presents growth models, time-related effects models, and polynomial models, in addition to the models which are commonly applied for panel data.
• Presents a complete treatment of quantile regression methods, including, estimation, inference issues and application of methods.
• Includes more than 250 examples divided into three groups of models (stacked, unstacked, and structured panel data), together with notes and comments.
• Delivers a balance between methodolgy and application.
• Provides guidance on which models not to use in a given scenario, along with advice on viable alternatives.
• Offers an overview of the recent developments in the quantile regression framework and why to use quantile regression in a variety of areas such as economics, finance and computing.
• Explores recent new developments in panel data analysis.
• Features a supporting website hosting datasets along with R, Stata and SAS software code.
An essential tool for advanced undergraduate or graduate students and applied researchers in finance, econometrics and population studies. Statisticians and data analysts involved with data collected over long time periods will also find this book a useful resource.
Researchers and PhD students in the field of statistics, economics, econometrics, social and environmental science and chemistry will benefit from this book. Researchers and PhD students in the field of statistics, economics, econometrics, social and environmental science and chemistry.
I Gusti Ngurah Agung, Graduate School of Management, Faculty of Economics, University of Indonesia
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Practitioners undertaking statistical analysis in applied research areas such the social, behavioural and environmental sciences.
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Handbook in Monte Carlo Handbook of Market Risk Simulation: Applications in Christian Szylar Financial Engineering, 978-1-118-12718-6 / 1-118-12718-8 432 pp. Pub: 23/12/13 Risk Management, and Statistics for Finance, Business & Economics Economics Authored by an acknowledged expert in the quantification of market risk, this one-stop guide conveniently and systematically displays all of the financial engineering topics, theories, applications, and current statistical methodologies that are intrinsic to the subject matter.
Paolo Brandimarte 978-0-470-53111-2 / 0-470-53111-8 608 pp. Pub: 26/05/14 Econometric & Statistical Methods
Understanding and investigating the impacts of market risk on the financial landscape is crucial in preventing crises. Written by a hedge fund specialist, the Handbook of Market Risk is the comprehensive guide to the subject of market risk. Featuring a format that is accessible and convenient, the handbook employs numerous examples to underscore the application of the material in a real-world setting. The book starts by introducing the various methods to measure market risk while continuing to emphasize stress testing, liquidity, and interest rate implications. Covering topics intrinsic to understanding and applying market risk, the handbook features: • An introduction to financial markets. • The historical perspective from market. • Value-at-risk. • Return and volatility estimates. • Diversification, portfolio risk, and efficient frontier. • The Capital Asset Pricing Model and the Arbitrage Pricing Theory. • The use of a fundamental multi-factors model. • Financial derivatives instruments. • Fixed income and interest rate risk. • Liquidity risk. • Alternative investments. • Stress testing and back testing • Banks and Basel II/III.
An accessible treatment of Monte Carlo methods, techniques, and applications in the field of finance and economics. • An introductory section for basic material on stochastic modeling and estimation aimed at readers who may need a summary or review of the essentials. • Carefully crafted examples in order to spot potential pitfalls and drawbacks of each approach. • An accessible treatment of advanced topics such as low discrepancy sequences, stochastic optimization, dynamic programming, risk measures, and Markov chain Monte Carlo methods. • Features an introductory section for basic material on stochastic modeling and estimation aimed at readers who may need a summary or review of the essentials. • Includes carefully crafted examples in order to spot potential pitfalls and drawbacks of each approach. • Presents an accessible treatment of advanced topics such as low discrepancy sequences, stochastic optimization, dynamic programming, risk measures, and Markov chain Monte Carlo methods. • Provides numerous pieces of R code used to illustrate fundamental ideas in concrete terms and encourage experimentation.
The Handbook of Market Risk is a must-have resource for financial engineers, quantitative analysts, regulators, risk managers in investments banks, and large-scale consultancy groups advising banks on internal systems. The handbook is also an excellent text for academics teaching postgraduate courses on financial methodology.
A complete reference for practitioners in the fields of finance, business, applied statistics, econometrics, and engineering, as well as a supplement for MBA and graduate-level courses on Monte Carlo methods and simulation.
Christian Szylar, PhD, is currently Global Head of Risk and Performance Management at Marshall Wace, LLP, one of the most prestigious asset management firms in financial hedge fund creation. Dr. Szylar is well published and speaks annually at well over two dozen international conferences on market risk subjects. This is his second book on the topic.
Paolo Brandimarte is Full Professor of Quantitative Methods for Finance and Logistics in the Department of Mathematical Sciences at Politecnico di Torino in Italy. Dr. Brandimarte is the author or coauthor of Introduction to Distribution Logistics, Quantitative Methods: An Introduction for Business Management, and Numerical Methods in Finance and Economics: A MATLAB-Based Introduction, Second Edition, all published by Wiley. S p r i n g / S u m m e r
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Multivariate Time Series Analysis: With R and Financial Applications
Measure, Probability, and Mathematical Finance: A Problem-Oriented Approach
Ruey S. Tsay
Guojun Gan, Chaoqun Ma, Hong Xie
978-1-118-61790-8 / 1-118-61790-8 520 pp. Pub: 20/01/14 Time Series
978-1-118-83196-0 / 1-118-83196-9 768 pp. Pub: 28/04/14 Statistics for Finance, Business & Economics
Every applied statistician, beginning financial analyst, and MBA graduate should have a copy of this complete set of multivariate time series tools from a leading authority. This book is the much anticipated sequel coming from one of the most influential and prominent experts on the topic of time series. Through a fundamental balance of theory and methodology, the book supplies readers with a comprehensible approach to financial econometric models and their applications to real-world empirical research.
Measure, Probability, and Mathematical Finance: A Problem-Oriented Approach features an introduction to the mathematical theory underlying the financial models that were developed and employed on Wall Street. This book aids in the understanding of how financial markets work, both in a theoretical and practical sense, by encouraging a problem-solving approach when applying mathematical analysis in real-life situations. Includes problems as well as detailed solutions to help readers learn the concepts and results quickly and promotes active learning by encouraging readers to write out solutions after considering the presented concepts, facts, and problems.
Differing from the traditional approach to multivariate time series, the book focuses on reader comprehension by emphasizing structural specification, which results in simplified parsimonious VAR MA modeling. Multivariate Time Series Analysis: With R and Financial Applications utilizes the freely available R software package to explore complex data and illustrate related computation and analyses. Featuring the techniques and methodology of multivariate linear time series, stationary VAR models, VAR MA time series and models, unitroot process, factor models, and factor-augmented VAR models, the book includes: • Over 300 examples and exercises to reinforce the presented content. • User-friendly R subroutines and research presented throughout to demonstrate modern applications. • Numerous datasets and subroutines to provide readers with a deeper understanding of the material.
• Presents definitions and theorems in a mathematically rigorous way. • Covers classic models in mathematical finance that have been developed and published since the seminal work of Black and Scholes. • Utilises a uniform set of symbols and notation throughout to further aid in reader comprehension and contains a comprehensive list of symbols at the end of the book. • Discusses how to address changes and fluctuations that need to be quantified as well as modeled and graphically visualized. As a textbook for introductory quantitative courses in business, economics, and the mathematics of finance at the upper-undergraduate and graduate levels; also appropriate for consumers and entrepreneurs who need to build their mathematical skills in order to better understand financial problems and make better financial choices; and academic and corporate libraries.
Upper undergraduate/graduate students. researchers and practitioners working in business, finance, and econometrics. Ruey S. Tsay, PhD, is H.G.B. Alexander Professor of Econometrics and Statistics at The University of Chicago Booth School of Business. Dr. Tsay has written over 125 published articles in the areas of business and economic forecasting, data analysis, risk management, and process control. A Fellow of the American Statistical Association, the Institute of Mathematical Statistics, and Academia Sinica, Dr. Tsay is author of Analysis of Financial Time Series, Third Edition and An Introduction to Analysis of Financial Data with R, and coauthor of A Course in Time Series Analysis.
Guojun Gan, PhD, is Director of Model Efficiency, Models, and Analytics at Manulife Financial in Toronto, Canada. Chaoqun Ma, PhD, is Professor and Dean of the School of Business Administration at Hunan University in China. Hong Xie, PhD, is Adjunct Professor in the Department of Mathematics and Statistics at York University in Canada.
Multivariate Time Series Analysis: With R and Financial Applications – Licensed: Simplified Chinese. M a t h s
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Statistics for Social Science
Computational Approaches to Studying the Coevolution of Networks and Behavior in Social Dilemmas
Analytical Sociology: Actions and Networks Gianluca Manzo
978-1-119-94038-8 / 1-119-94038-9 448 pp. Pub: 12/05/14 Statistics for Social Sciences
978-1-118-63687-9 / 1-118-63687-2 184 pp. Pub: 21/04/14 Statistics for Social Sciences
Analytical Sociology: Actions and Networks presents the most advanced theoretical discussion of analytical sociology, along with a unique set of examples on mechanism-based sociology. Leading scholars apply the theoretical principles of analytical sociology to understand how puzzling social and historical phenomena including crime, lynching, witch-hunts, tax behaviours, Web-based social movement and communication, restaurant reputation, job search and careers, social network homophily and instability, cooperation and trust are brought about by complex, multi-layered social mechanisms. The analyses presented in this book rely on a wide range of methods which include qualitative observations, advanced statistical techniques, complex network tools, refined simulation methods and creative experimental protocols. This book ultimately demonstrates that sociology, like any other science, is at its best when it dissects the mechanisms at work by means of rigorous model building and testing.
Presents an alternative approach to studying co-evolution of social networks and behaviour in social dilemmas using computation methods, rather than mathematical analysis, to answer research questions. Computational Approaches to Studying the Co-evolution of Networks and Behaviour in Social Dilemmas shows students, researchers, and professionals how to use computation methods, rather than mathematical analysis, to answer research questions for an easier, more productive method of testing their models. Illustrations of general methodology are provided and explore how computer simulation is used to bridge the gap between formal theoretical models and empirical applications. An accompanying website supports the text. • Presents an alternative approach to studying co-evolution of social networks and behaviour in social dilemmas using computation methods, rather than mathematical analysis, to answer research questions.
• Looks at a wide range of complex social phenomena within a single and unitary theoretical framework.
• Discusses two theoretical models for co-evolution of social networks and behaviour in social dilemmas.
• Explores a variety of advanced methods to build and test theoretical models.
• Each model focuses on a different type of social dilemma.
• Examines how both computational modelling and experiments can be used to study the complex relation between norms, networks and social actions.
• Features model studies on cooperation problems and coordination problems. • Illustrates how further predictions can be derived through computational simulation.
• Brings together research from leading global experts in the field in order to present a unique set of examples on mechanism-based sociology.
• Supporting by an accompanying website. Academic researchers, postgraduate students, sociologists, economists, and other social scientists, with an interest in social dilemma research, social network analysis and computational methods. Researchers interested in social dilemma research, network dynamics and the co-evolution of networks and behaviour.
Students and researchers. Gianluca Manzo, Research Fellow in Sociology, National Centre of Scientific Research, Paris, France
Rense Corten¸ Department of sociology/ICS (Interuniversity Centre for Social Science, Theory and Methodology), Utrecht University, The Netherlands.
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Bayesian Inference in the Social Sciences
Statistics for Exercise Science and Health with Microsoft Office Excel
Ivan Jeliazkov, Xin-She Yang
J. P. Verma
978-1-118-77121-1 / 1-118-77121-4 352 pp. Pub: 14/07/14 Bayesian Analysis
978-1-118-85521-8 / 1-118-85521-3 752 pp. Pub: 02/06/14 Sports Science
Bayesian Inference in the Social Sciences builds upon the recent growth in Bayesian methodology and examines an array of topics in model formulation, estimation, and applications. Particular emphasis is placed on an interdisciplinary coverage, model checking, and modern computational tools such as Markov chain Monte Carlo.
This book introduces the use of statistics to solve a variety of problems in exercise science and health and provides readers with a solid foundation for future research and data analysis. • Aids readers in analyzing their own data using the presented statistical techniques combined with Excel.
The book's broad interdisciplinary coverage provides exposure to recent and trending developments in a diverse, yet closely integrated, set of research topics in the social sciences. This approach facilitates the transmission of new ideas, developments, and methodology from one discipline to another, while at the same time maintaining manageability, coherence, and a clear focus. • Addresses state-of-the-art modeling and estimation techniques and provides related computer code and estimation algorithms via the book's related website. • Presents real-world applications and case studies including asset pricing under fat-tailed distributions, price indifference modeling and market segmentation, analysis of dynamic networks, and ethnic minorities and civil war. • Builds upon recent growth in Bayesian methodology and examines an array of topics in model formulation, estimation, and applications. • Advances new theoretical developments and modeling approaches as well as discusses the formulation and analysis of models with partial observability, sample selection, and incomplete data.
• Features comprehensive coverage of hypothesis testing and regression models to facilitate modeling in sports science. • Utilizes Excel to enhance reader competency in data analysis and experimental designs. • Includes coverage of both binomial and poison distributions with applications in exercise science and health. • Provides solved examples and plentiful practice exercises throughout in addition to case studies to illustrate the discussed analytical techniques. • Contains all needed definitions and formulas to aid readers in understanding different statistical concepts and developing the needed skills to solve research problems. As a textbook for graduate and PhD-level courses in exercise science and health for students of any applied branch of physical education and sports sciences, such as sports psychology, sports kinesiology, sports management, sports biomechanics, health science, health education, exercise science, and nutrition; as a reference for professionals and sports scientists in physical education and sports as well as academics of physical education, sports, and its allied disciuplines; and as a reference in academic and public libraries.
Researchers, graduates. Ivan Jeliazkov, PhD,is Associate Professor of Economics and Statistics at the University of California, Irvine. He is a member of the American Economic Association, American Statistical Association, Econometric Society, and International Society for Bayesian Analysis. Xin-She Yang, PhD, is Senior Research Scientist in the Department of Mathematical and Scientific Computing at the National Physical Laboratory, UK, Reader in Modeling and Optimization at Middlesex University, UK, and Adjunct Professor at Reykjavik University, Iceland. He is Editor-inChief of International Journal of Mathematical Modelling and Numerical Optimization, a member of both the Society for Industrial and Applied Mathematics and the British Computer Society, a Fellow of The Royal Institution of Great Britain, and author of seven additional books and over 100 journal articles.
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J. P. Verma, PhD, is Professor of Statistics in the Department of Sport Psychology at Lakshmibai National Institute of Physical Education. Dr. Verma has been an active researcher in data analysis and sports statistics and has is the author of five additional books. He has conducted many workshops on research methodology, research designs, multivariate analysis, statistical modeling, and data analysis for students of management, physical education, social science, and economics.
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The Wellbeing of Nations: Meaning, Motive and Measurement
Social Networks and their Economics: Influencing Consumer Choice
Paul Allin, David J. Hand
978-1-118-48957-4 / 1-118-48957-8 300 pp. Pub: 10/07/14 Econometric & Statistical Methods
978-1-118-45765-8 / 1-118-45765-X 216 pp. Pub: 24/09/13 Statistics for Social Sciences
What is national wellbeing and what is progress? Why measure these definitions? Why are measures beyond economic performance needed and how will they be used? How do we measure national wellbeing & turn the definitions into observable quantities? Where are we now and where to next? These questions are asked and answered in this much needed, timely book.
Intuitively, we all appreciate that we can be influenced by our friends and peers in what we do, how we behave, and what products we consume. Until recently, it has been difficult to measure this interdependence, mainly because data on social networks was difficult to collect and not readily available. More and more companies such as mobile phone carriers or social networking sites such as Facebook are collecting such data electronically. Daniel Birke illustrates in compelling real-world case studies how companies use social networks for marketing purposes and which statistical analysis and unique datasets can be used. • Explores network effects and the analysis of social networks, whilst providing an overview of the state-of-the art research. • Looks at consumption interdependences between friends and peers: Who is influencing who through which channels and to what degree? • Presents statistical methods and research techniques that can be used in the analysis of social networks. • Examines SNA and its practical application for marketing purposes. • Features a supporting website featuring SNA visualisations and business case studies. • Considers how network effects influence the market structure of the telecommunications industry. • Covers both traditional research approaches in economics and the more recent social network research.
The Wellbeing of Nations provides an accessible and comprehensive overview of the measurement of national well-being, examining whether national wellbeing is more than the sum of the wellbeing of everyone in the country, and identifying and reviewing requirements for new measures. It begins with definitions, describes how to operationalize those definitions, and takes a critical look at the uses to which such measures are to be put. The authors examine initiatives from around the world, using the UK ‘measuring national wellbeing programme’ as a case study throughout the book, along with case studies drawn from other countries, as well as discussion of the position in some countries not yet drawn into the national wellbeing scene. Paul Allin, recently retired from the senior civil service where he was most recently the director of the measuring national well-being programme in the Office for National Statistics (ONS) and chaired the programme’s Technical Advisory Committee. Paul is a Chartered Statistician and worked for nearly 40 years in a number of government departments and agencies, including as the chief statistician and head of social policy in the Department for Culture, Media and Sport. He is actively involved in the Royal Statistical Society (RSS) and was an Honorary Secretary for 6 years. David Hand, was Professor of Statistics at Imperial College for eleven years, and is now Emeritus Professor of Mathematics. He has a particular interest in measurement and has written various papers on this topic. Statistical Society.
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Post-graduate students. Marketing practitioners. Dr Daniel Birke, Visiting Researcher, Aston Business School; and Senior Consultant, Ernst & Young, Watford, UK. Daniel's main research interest is looking at how social networks influence consumer behaviour. He has frequently presented his research at conferences around the world and has published in leading academic journals. He has frequently developed and led workshops on how to use social network analysis in marketing. Currently he works as a consultant in Ernst & Young's Advisory business, advising large companies in their sales and marketing strategy. He is also a non-executive director at Rocketfish, the UK's leading search-engine marketing company.
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