math_&_stats

Mathematics & Statistics Rights Guide Autumn/Winter 2012

For more information on any of these titles please contact Julie Attrill [email protected]

M a t h s & S t a t i s t i c s 1 A u t u m n / W i n t e r 2 0 1 2

Maths & Statistics Rights Guide: Autumn / Winter 2012

Mathematics ......................................................................... 3

Quantum Dynamics for Classical Systems: With Applications of the Number Operator/Bagarello .............................................................................................................................................................. 3

Combinatorics: An Introduction/Faticoni ........................................................................................... 3

Public Key Cryptography: Applications and Attacks/Batten ........................................................... 4

Probability & Statistics........................................................ 4

Spatio-temporal Design: Advances in Efficient Data Acquisition/Mueller ...................................... 4

Spatial and Spatio-Temporal Geostatistical Modeling and Kriging/Fernández-Avilé ..................... 5

Introduction to Statistics Through Resampling Methods and R, 2e/Good ..................................... 5

An Accidential Statistician: The Life and Memories of George E.P. Box/Box ................................ 6

Willful Ignorance: The Blind Side of Statistics/Weisberg ................................................................. 6

General Theory of Coherent Lower Previsions/Troffaes .................................................................. 7

An Introduction to Imprecise Probabilities/Coolen .......................................................................... 7

Information Search After Static or Moving Targets: Theory and Modern Applications/Ben-Gal . 8

Computational and Statistical Methods for Protein Quantification by Mass Spectrometry/Eidhammer.................................................................................................................... 8

Visual Data Mining: The VisMiner Approach/Anderson .................................................................... 9

The R Book, 2e/Crawley ...................................................................................................................... 9

Improving Surveys with Process and Paradata/Kreuter................................................................. 10

Methods and Applications of Statistics in the Atmospheric and Earth Sciences/Balakrishnan . 10

Nonparametric Predictive Inference/Coolen ................................................................................... 11

Categorical Data Analysis 3e/Agresti ............................................................................................... 11

An Introduction to Social Network Analysis with Applications on Organizational Risk/McCulloh ............................................................................................................................................................ 12

Biostatistics and Clinical Trials ....................................... 12

Population-based Cancer Survival Analysis/Dickman ................................................................... 12

Statistical Modelling of ICU Data/Chevret........................................................................................ 13

Understanding Large Temporal Networks and Spatial Networks: Exploration, Pattern Searching, Visualization and Network Evolution/Doreian ................................................................................. 13

Multiple Imputation and its Application /Carpenter ........................................................................ 14

Applied Missing Data in the Health Sciences/Zhou ........................................................................ 14

Longitudinal Data Analysis, 2e/Hedeker ......................................................................................... 15

How to Design, Analyse and Report Cluster Randomised Trials in Medicine and Health-Related Research/Campbell ............................................................................................................................ 15

An Introduction to Adaptive Designs With Applications to Clinical Trials Using R/Chernick ..... 16

Statistics for Engineering ................................................. 16

Problem Solving and Data Analysis Using Minitab: A Clear and Easy Guide to Six Sigma Methodology/Khan ............................................................................................................................ 16

2 C o n t a c t : j a t t r i l l @ w i l e y . c o m

A First Course in Probability and Markov Chains/Modica ............................................................. 17

Optimal Redundancy Allocation: With Practical Statistical Applications and Theory/Ushakov . 17

Probabilistic Reliability Models/Ushakov ........................................................................................ 18

Statistics for Finance, Business and Economics .......... 18

Handbook of Decision Analysis/Parnell .......................................................................................... 18

Handbook of Financial Risk Management/Chan ............................................................................. 19

Financial Risk Modelling and Portfolio Optimization with R/Pfaff ................................................ 19

Statistics for the Social Sciences .................................... 20

Applied Time Series Analysis for the Social Sciences: Specification, Estimation, and Inference/Baker .................................................................................................................................. 20

Research Methods for Postgraduates, 3e/Greenfield ..................................................................... 20


Mathematics

Quantum Dynamics for Classical Systems: With Applications of the Number Operator

F. Bagarello

978-1-118-37068-1 / 1-118-37068-6

218 pp. Pub: 14/01/13

Mathematics

With a focus on the relationship between quantum mechanics and social science, this book introduces the main ideas of number operators while avoiding excessive technicalities that are not necessary to understand and learn the various mathematical applications.

• Discusses the use of mathematical tools that are related to quantum mechanics and features applications in several contexts including finance, biology, and social science.

• Features applications across diverse fields including stock markets and population migration and provides a unique quantum perspective on these classes of models.

• Systematically shows how to use creation and annihilation operators for classical problems.

• Addresses the recent increase in research and literature on the many applications of quantum tools in applied mathematics.

• Ideal for applied mathematicians and physicists who need to expand their mathematical background to address problems from biology, psychology, economics, and the social sciences.

• Written by a well-known physicist who clarifies numerous misunderstandings and misnomers while shedding light on new approaches in this growing area.

As a reference for researchers, professionals, and academics in applied mathematics, economics, physics, biology, and sociology who need to expand their knowledge of classical applications of quantum tools; as a resource for information on recent and related trends; appropriate as a graduate-level and/or PhD-level book for courses on dynamical systems, quantum mechanics, and mathematical models in physics, biology, sociology, and economics; and academic libraries.

Fabio Bagarello, Professor, Department of Mathematical Methods & Models, University of Palermo, Italy, is author of approximately 100 journal articles,

Combinatorics: An Introduction

Theodore G. Faticoni

978-1-118-40436-2 / 1-118-40436-X

288 pp. Pub: 14/01/13

Mathematics

This book provides a treatment of counting combinatorics that uniquely includes both detailed formulas and proofs and features coverage of derangements, elementary probability, conditional probability, independent probability, and Bayes' Theorem.

• Includes plenty of worked examples, proofs, and exercises in every chapter.

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

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

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

• Includes elementary applications that do not advance beyond the use of Venn diagrams, the inclusion/exclusion formula, the multiplication principal, permutations, and combinations.

As a text or supplement for discrete and/or finite mathematics courses at the upper-undergraduate level; as a reference for professors, teaching assistants, adjunct instructors, or anyone who wants to learn the various applications of elementary combinatorics; as recommended reading for teachers of mathematics and computer science who would like to convey a better understanding and appreciation of the field and apply it to their students; and academic and public libraries.

Theodore G. Faticoni is Professor in the Department of Mathematics at Fordham University. He received his PhD in Mathematics from the University of Connecticut in 1981. His professional experience includes 30 research papers in peer reviewed journals and 40 lectures on his research to his colleagues.


Public Key Cryptography: Applications and Attacks

L. Batten

978-1-118-31712-9 / 1-118-31712-2

253 pp. Pub: 15/02/13

Cryptography

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

The first chapters of this text cover the theory of public key systems in current use, including ElGamal, RSA, Elliptic Curve and digital signature schemes. The underlying mathematics needed to build and study these schemes is provided as needed through the book. The latter half of the book examines attacks on these schemes via the mathematical problems on which they are based; these are the discrete logarithm problem and the difficulty of factoring integers.

Each section of the book contains up to ten examples, including several which are computationally challenging and therefore need software support. The solutions to these examples are described in detail. In addition, each chapter contains forty to fifty problems for the reader and full solutions are provided in the appendix.

• Explains fundamentals of Public Key Cryptography.

• Illustrated with many examples and exercises.

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

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

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

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

Probability & Statistics

Spatio-temporal Design: Advances in Efficient Data Acquisition

Werner Mueller, Jorge Mateu

978-0-470-97429-2 / 0-470-97429-X

384 pp. Pub: 30/01/13


A state-of-the-art presentation of optimum spatio-temporal sampling design - bridging classic ideas with modern statistical modeling concepts and the latest computational methods.

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

• Discusses basic methods and distinguishes between design and model-based approaches to collecting space-time data.

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

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

• Includes real data sets, data generating mechanisms and simulation scenarios.

• Accompanied by a supporting website featuring R code.

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

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


Spatial and Spatio-Temporal Geostatistical Modeling and Kriging

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

978-1-118-41318-0 / 1-118-41318-0

400 pp. Pub: 15/04/13


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

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

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

Introduction to Statistics Through Resampling Methods and R, 2e

Phillip I. Good

978-1-118-42821-4 / 1-118-42821-8

288 pp. Pub: 15/04/13


On the First Edition: "This is certainly one of the most impressive little paperback 200-page introductory statistics books that I will ever see...it would make a good nightstand book for every statistician." (Technometrics, May 2006)

This book provides a quick and highly accessible study of an alternative approach to understanding basic statistics. Written in an informal, highly accessible style, this text is an excellent guide to descriptive statistics, estimation, testing hypotheses, and model building. All the pedagogical tools needed to facilitate quick learning are provided, including:

• More than two hundred and fifty exercises, now with selected "hints," scattered throughout the text to stimulate readers' thinking and to actively engage them in applying their newfound skills.

• An increased focus on why a method is introduced.

• Multiple explanations of basic concepts.

• Real-life applications in customer-related, statistics-related disciplines.

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

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

• An instructor's manual that provides answers to exercises in the book.

This text or supplement serves as an excellent introduction to statistics for students in all disciplines. The accessible style and focus on real-life problem solving are perfectly suited for both students and practitioners. It makes for light reading with practitioners of statistics who are eager to learn an alternative approach to understanding basic concepts.

Phillip I. Good has published more than thirty scholarly works, more than 600 articles, and fourteen books, including Common Errors in Statistics (and How to Avoid Them) and A Manager's Guide to the Design and Conduct of Clinical Trials, both from Wiley.


An Accidential Statistician: The Life and Memories of George E.P. Box

George E. P. Box

978-1-118-40088-3 / 1-118-40088-7

288 pp. Pub: 18/03/13


In An Accidental Statistician, acclaimed statistician George E.P. Box offers a narrative of his life, from his early childhood on to his celebrated career in academia and industry. While many interviews and articles have been published about Box, this memoir is the only first-hand account of his professional accomplishments and personal insights, written in the engaging, charming manner unique to the author.

Regardless of their academic background, readers will be intrigued by the life and story behind a rising academician as the professional accomplishments of Box or are simply are interested in the life and times of an accomplished academician.

The memoir opens with two Forewords written by Box's former colleagues and closest confidants. Celebrating his countless accomplishments and impact on the statistics and engineering communities, more than a dozen researchers and practitioners provide their thoughts and accounts of how Box touched their careers and lives. The author's stories and personal insights are accompanied by numerous, previously unpublished photos from Box's personal collection.

As a general interest read for statisticians, engineers, historians, or anyone from the undergraduate to professional level who would like to learn more about the history behind major developments in the fields of statistics and systems engineering; academic libraries.

George E.P. Box is Ronald Aylmer Fisher Professor Emeritus of Statistics & Industrial Engineering, University of Wisconsin-Madison. He is a Fellow of the Royal Society of London, the American Academy of Arts and Sciences as well as an honorary Fellow and Shewart and Deming Medalist of the American Society for Quality and an honorary member of the International Statistical Institute. He is also the recipient of the Samuel S. Wilks Memorial Medal of the American Statistical Association and the Guy Medal in Gold of the Royal Statistical Society. Dr. Box is the co/author of many titles published by Wiley.

Willful Ignorance: The Blind Side of Statistics

Herbert I. Weisberg

978-0-470-89044-8 / 0-470-89044-4

320 pp. Pub: 15/04/13


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

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

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

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

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

• Speculations about the possible future evolution of statistics are envisioned.

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

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

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


General Theory of Coherent Lower Previsions

Matthias Troffaes, Gert de Cooman

978-0-470-72377-7 / 0-470-72377-7

288 pp. Pub: 26/02/13


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

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

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

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

• Includes illustrative examples to support the understanding of the main concepts.

• 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.

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, has published papers in a variety of journals, and written two book chapters. Gert de Cooman, SYSTeMS Research Group, Ghent University, Belgium has many years' research and teaching experience. He serves/has served on the Editorial Boards of many statistical journals, publishing over 40 journal articles, and is an editor of the Imprecise Probabilities Project. He has also written chapters for six books, and has co-edited four.

An Introduction to Imprecise Probabilities

Frank Coolen, Matthias Troffaes, Gert de Cooman

978-0-470-97381-3 / 0-470-97381-1

416 pp. Pub: 11/03/13


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

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

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

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

• Each chapter is written by leading experts in their field.

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

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

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


Information Search After Static or Moving Targets: Theory and Modern Applications

Irad Ben-Gal, Eugene Kagan

978-0-470-97393-6 / 0-470-97393-5

320 pp. Pub: 01/30/13


This text looks at search algorithms that are applicable in various practical settings. With the implementation of suitable data structures and metrics within the suggested search algorithms, the book presents a unified framework to demonstrate immediate merits and applications. It also looks at applying information theory to search models and algorithms on graphs that can be represented by Markov decision processes and demonstrates that such an approach can lead to the construction of optimal search plans and policies.

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

• Extremely relevant for classification of data records.

• Presents a unified framework for the methods currently available and demonstrates their integration.

• The algorithms discussed will be implemented in C++ programs, available on an accompanying website.

R&D engineers and researchers in the field of data mining and knowledge engineering. Graduate students in engineering and applied mathematics.

Computational and Statistical Methods for Protein Quantification by Mass Spectrometry

Ingvar Eidhammer

978-1-119-96400-1 / 1-119-96400-8

320 pp. Pub: 18/03/13


Mass spectrometry is a powerful analytical technique that is used to identify unknown compounds, quantify known materials, and elucidate their molecular structure and chemical composition. In mass spectrometry (MS) based protein quantification, computational tools are essentially required to process the large amounts of generated data.

This book aims to systemize and describe the different approaches used for performing protein quantification by mass spectrometry, providing the understanding of computational and statistical methods used for analyzing the data from such experiments. Understanding this process is important for bioinformaticians developing software for protein quantification and allows biologists that use the software to become better equipped to plan their experiments and interpret the obtained results. Numerous examples are featured throughout the book as well as exercises and an accompanying solution manual, available online.

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


Visual Data Mining: The VisMiner Approach

Russell Anderson

978-1-119-96754-5 / 1-119-96754-6

208 pp. Pub: 31/12/12


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

This book presents data mining tools, data visualisations and provides a comprehensive set of non-trivial datasets and problems with accompanying software that support students in learning, understanding, and practicing the entire data mining process.

Visual Data Mining introduces the methodology for conducting data mining analysis along with data visualisation approaches to data mining using VisMiner. VisMiner is a data mining tool that has been developed specifically to bridge the gap between theory and practice.

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

• Introduces data visualisation approaches to data mining using VisMiner.

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

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

• Provides a data mining step checklist supported by VisMiner tools.

• Includes a free student license for VisMiner.

Graduate students and researchers involved in graphical modeling, applied statistics or business intelligence. Business intelligence analysts and statisticians, compliance and financial experts in both commercial and government organisations across all industry sectors.

Russell K. Anderson, Information & Decision Management Department, West Texas A&M University, USA.

The R Book, 2e

Michael J. Crawley

978-0-470-97392-9 / 0-470-97392-7

1008 pp. Pub: 30/01/13


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

This edition introduces the advantages of the R environment, in a user-friendly format, to beginners and intermediate users in a range of disciplines, from science and engineering to medicine and economics. The format enables it to be either read as a text, or dipped-into as a reference manual.

The early chapters assume no background in statistics or computing, and introduce the reader to the basic concepts involved. In this way the reader is introduced to the assumptions that lie behind the tests, fostering a critical approach to statistical modeling. These early chapters have been thoroughly updated to take account of the way language has evolved since the publication of the first edition. Subsequent chapters examine more advanced topics, cementing what is learnt in the opening chapters, as well as benefiting more intermediate readers. Throughout the book, the reader's experience is furthered by practical guidance and the inclusion of numerous worked examples.

• Introduces a clear structure/organisation with numbered section headings to help readers locate information more efficiently.

• Revised to account for the evolution of R over the past five years.

• Now includes links to other languages, such as C and Fortran, as well as R.

• Supported by a website allowing examples from the text to be run by the user.

• Features a new chapter on Bayesian Analysis.

Senior undergraduates, postgraduates and professionals in science, engineering and medicine. Senior undergraduates, postgraduates and professionals in economics, geography and the social sciences.

Michael J. Crawley, Department of Biological Sciences, Imperial College of Science, Technology & Medicine is author of three bestselling Wiley statistics titles and five life science books.

Previous edition – Licensed: Japanese

1 0 C o n t a c t : j a t t r i l l @ w i l e y . c o m

Improving Surveys with Process and Paradata

Frauke Kreuter

978-0-470-90541-8 / 0-470-90541-7

320 pp. Pub: 11/03/13


From a reviewer, "[This] is a hot topic. [It] is an area, which is still in its infancy, but it has ... reached the stage when the material can be codified into a book. The proposal is well suited to the series and Frauke Kreuter and the proposed team are very appropriate people to do it."

The objective of the book is to provide readers with an overview of the best practices and cutting-edge research on the new topic of paradata in order to improve survey quality and total survey error.

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

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

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

• Best practices are emphasised throughout.

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

The book is aimed at both producers and users of survey data. It can be used by researchers from academia, government, and the private sector. It will complement classes on data collection, survey methodology, and nonresponse and measurement error. It will have global appeal.

Frauke Kreuter is Associate Professor in the Joint Program in Survey Methodology at the University of Maryland. She is Associate Editor of a number of key journals in her field of study. She is the author of Data Analysis Using Stata, Second Edition (2008). She has published over two dozen articles on the topic of paradata on which she is considered to be one of the world's foremost authorities.

Methods and Applications of Statistics in the Atmospheric and Earth Sciences

N. Balakrishnan

978-0-470-50344-7 / 0-470-50344-0

352 pp. Pub: 03/12/12


Based on the multifaceted Encyclopedia of Statistical Sciences, 2

nd Edition, this concise book outlines the

statistical concepts and applications that are essential for understanding modern research data gathered in the earth and atmospheric sciences.

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

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

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

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

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

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

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

M a t h s & S t a t i s t i c s 1 1 A u t u m n / W i n t e r 2 0 1 2

Nonparametric Predictive Inference

Frank Coolen

978-0-470-72334-0 / 0-470-72334-3

256 pp. Pub: 07/08/13


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

After the initial introductory chapter, the book provides a series of chapters outlining the use of NPI in specific settings, e.g. for real-valued random quantities or for multinomial data. This will be followed by chapters detailing further applications in statistics, providing examples such as NPI for statistical quality and process control, reliability and operations research, with a variety of examples such as maintenance and replacement problems, queuing situations and risk reliability inferences.

The foundations and ideas behind NPI will be presented along with an examination and comparison of more traditional approaches of classical and Bayesian statistics, providing further insights into the advantages of NPI. Future directions and the accommodation of multivariate data will also be discussed.

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

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

• Minimal background knowledge of basic mathematics and statistics is needed.

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

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

Categorical Data Analysis 3e

Alan Agresti

978-0-470-46363-5 / 0-470-46363-5

768 pp. Pub: 19/11/12


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

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

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

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

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

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

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

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

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

Designed as a reference book for statisticians and biostatisticians as well as scientists and graduate students practicing statistics or as a serious one or two semester textbook at the upper undergraduate or beginning graduate level in analysis of multivariate and categorical data

Alan Agresti, is Distinguished Professor in the Department of Statistics at the University of Florida. He has published extensively on categorical data methods and has presented courses on the topic for universities, companies, and professional organisations worldwide. A Fellow of the American Statistical Association, he is also the author of two other Wiley texts on categorical data analysis and coauthor of Statistical Methods for the Social Sciences.

Previous editions – Licensed: Korean, Simplified Chinese

An Introduction to Categorical Data Analysis all editions – Licensed: Japanese, Korean, Orthodox Chinese, Simplified Chinese.


An Introduction to Social Network Analysis with Applications on Organizational Risk

Ian McCulloh, Helen Armstrong, Anthony Johnson

978-1-118-16947-6 / 1-118-16947-6

250 pp. Pub: 08/04/13

Operations Research & Management Science

Authored by military and intelligence professionals, this comprehensive book on the new and emerging topic of Social Network Analysis introduces network analysis and hones in on basic centrality measures, social links, subgroup analysis, data sources and more.

• Includes practice problems and exercises.

• Contains examples of calculations and formulas to illustrate mathematical calculations for social network measures.

• Authored by professionals who have trained soldiers in Iraq and Afghanistan, local and national police, and other industry professionals on applications of social network analysis.

• Covers content in an accessible way for both practitioners and students.

Practitioners in management, intelligence and law enforcement who wish to learn and apply social network analysis to their respective fields. Also, those who teach workshops in social network analysis. Final year undergraduates and entry-level graduate students.

Ian A. McCulloh Assistant Professor, Dept. of Behavioral Sciences & Leadership, West Point. MAJ (Major) McCulloh has served on operational assignments with the 10

th

Mountain Division, Commander of Weapons of Mass Destruction Detachment in the 1

st Special Forces Group

(Airborne), and Commander of the 11th Chemical Company.

Currently, he is working on modeling and detecting statistically significant changes in networks, and methods for unobtrusive geo-location and social network data collection for the Network Science Center. Helen Armstrong, Associate Professor, School of Information Systems, Curtin University of Technology, Perth, Australia, has more than 20 years’ experience in teaching and researching in ICT network security, analyses of networks and systems, information systems strategy and management and problem solving in business environments. Anthony Johnson, Professor, Dept. of Mathematics, US Military Academy, West Point, where he combines signal communications with his background in linear algebra to explore and exploit networks. Afghanistan.

Biostatistics and Clinical Trials

Population-based Cancer Survival Analysis

Paul Dickman, Timo Hakulinen

978-0-470-02859-9 / 0-470-02859-9

320 pp. Pub: 06/02/13


There has been increased interest in studying cancer patient survival in recent years, which has prompted advances in methods for estimating and modeling cancer patient survival. This book is the first focused on this topic, and uses real data and software to illustrate the methods involved. The supporting website provides code to enable readers to reproduce the analysis top illustrate the examples included in the book. The book presents methods for population-based cancer survival analysis, that is, the analysis of patient survival using data collected by population-based cancer registries. The primary focus will be on the statistical methods but non-statistical issues that arise in population-based studies of cancer patient survival, such as registration, coding and classification, and follow up procedures are also discussed.

• There are a number of books on clinical survival studies, however this will be the first to focus on the concept of relative survival analysis.

• Based upon the authors' numerous successful courses in population-based cancer survival analysis.

• Supporting website to include data and specially developed SAS and Stata code.

• Presents methods that can be applied to the study of other diseases and to non-population based studies.

• Part of the Statistics in Practice: Human & Biological Sciences Series.

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

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


Statistical Modelling of ICU Data

Sylvie Chevret, Matthieu Resche-Rigon, Romain Pirracchio

978-1-119-97926-5 / 1-119-97926-9

288 pp. Pub: 03/18/13


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

This book presents the statistical approaches, with special focus on innovative approaches, that allow handling of the specificities of ICU data.

The book covers the various clinical endpoints used in ICU, notably the different measures of mortality, of nosocomial events, of durations (lengths of stay, durations of support, etc.), as well as the modelling of severity scores or biological measures over time, ie, the analysis of repeated measures.

Furthermore, the book will deal with data from randomised clinical trials, with some emphasis on the RCT that ought to be done in pandemic settings (such as H1N1), as well as data from observational nonrandomised studies, including registries that are often used in ICU or assessment of care that could not be easily randomly assigned (such as ICU care itself). The performances measures in ICU will be addresses and will look at the centre effect and the volume effect, since there are mostly used to compare ICUs.

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

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

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

Patrick Doreian, Vladimir Batagelj, Anuska Ferligoj, Natasa Kejzar

978-0-470-71452-2 / 0-470-71452-2

416 pp. Pub: 06/02/13


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

• Authors are leaders in this evolving subject.

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

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

• Examines evolutionary networks generative and network mechanisms.

• Supported by downloadable datasets via website link.

• Features bibliometric dynamics and the structure of science.

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

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


Multiple Imputation and its Application

James Carpenter, Michael Kenward

978-0-470-74052-1 / 0-470-74052-3

352 pp. Pub: 01/21/13


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

Imputation is the substitution of some value for a missing data point or a missing component of a data point. Once all missing values have been imputed, the dataset can then be analyzed using standard techniques for complete data.

This book is written with three main aims; to provide a thorough introduction to the general MI methods, to provide a detailed discussion of the practical use of the MI method and to present real-world examples drawn from the field of biostatistics. Illustrated throughout, using different issues that arise in the use of MI in observational and clinical trial settings. Relevant computer code and data will be provided for the examples used throughout the book and will include SAS, Stata, WinBUGS, MLwiN and R.

• Provides an introduction to general multiple imputation methods.

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

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

• Features a number of detailed case studies that show how the techniques can be applied in practice.

• Illustrates the use of MI in SAS, Stata, WinBUGS, MLwiN and R.

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

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

James Carpenter, Medical Statistics Unit, London School of Hygiene and Tropical Medicine, UK. Michael G. Kenward, Medical Statistics Unit, London School of Hygiene and Tropical Medicine, UK. Amongst other areas Professor Kenward has worked in pre-clinical and clinical medicine and epidemiology for over twenty years, holding a number of international positions. He has also been a statistical consultant for over twenty years, predominantly in medical research. He has taught over 80 short courses in biostatistics throughout the world, and is the author of the book Analysis of Repeated Measurements.

Applied Missing Data in the Health Sciences

Xiao-Hua (Andrew) Zhou, Leslie Taylor, Chuan Zhou

978-0-470-52381-0 / 0-470-52381-6

384 pp. Pub: 18/03/13


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.

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

• 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 upper-undergraduate and graduate levels; as a resource for health science researchers and applied statisticians; and academic libraries.

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


Longitudinal Data Analysis, 2e

Donald Hedeker, Robert D. Gibbons

978-0-470-88918-3 / 0-470-88918-7

448 pp. Pub: 06/10/13


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

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

Specific statistical procedures covered within the book include: repeated measures analysis of variance, multivariate analysis of variance for repeated measures, random-effects regression models (RRM), covariance-structure models, generalized-estimating equations (GEE) models, and generalizations of RRM and GE for categorical outcomes.

This book emphasises methods for analysis of longitudinal data analysis, which are extensively illustrated using real examples. The authors have chosen not to focus on software in the book, though some syntax examples are provided. Many programs are available for the analyses presented in this book including SAS, SPSS, SYSTAT, HLM, MLwiN, MIXREG/MIXOR and Mplus.

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

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

How to Design, Analyse and Report Cluster Randomised Trials in Medicine and Health-Related Research

Michael J. Campbell, Stephen J. Walters

978-1-119-99202-8 / 1-119-99202-8

320 pp. Pub: 04/03/13


Health technology assessment often requires evaluation of interventions implemented for clusters of individuals at the level of the health service organisation unit.

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

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

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


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

Michael R. Chernick, Kenneth N. Anderson

978-0-470-40445-4 / 0-470-40445-0

384 pp. Pub: 14/01/13


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

• Practical examples are illustrated throughout the book.

• Plentiful exercises are presented to further reinforce the concepts discussed.

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

• The R language is employed throughout.

• Bayesian influences are showcased when appropriate.

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

• Historical developments of the methodology are presented as sidebars to the theory.

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

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

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

Statistics for Engineering

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

Rehman M. Khan

978-1-118-30757-1 / 1-118-30757-7

390 pp. Pub: 30/01/13


Provides, in book form, a training course for a much sought-after skill set in both industry and academia.

Six Sigma is probably the most popular management methodology used to improve processes by eliminating defects. It is used in various types of organizations ranging from local government to hospitals and large corporations & engineering companies. This book aims to enable readers to understand and implement, via the widely used statistical software package Minitab (Release 16), statistical methods fundamental to the Six Sigma approach to the continuous improvement of products, processes and services.

• Provides readers with a step by step guide to problem solving and statistical analysis using MINITAB 16 (also compatible with 15).

• Contains hundreds of screenshots to aid the reader, along with explanations of the statistics being performed and interpretation of results.

• Presents the basic statistical techniques used by Six Sigma Black Belts.

• Keeps mathematical theory to a minimum.

• Contains examples, exercises and solutions throughout, and supported by an accompanying website housing data sets.

Numerical professionals who want to learn to use Minitab 16 for data analysis, problem solving and process improvement. Undergraduate or postgraduate level for academic courses incorporating. Six Sigma Green and Black Belts, from organisations that use Minitab.

Rehman M. Khan, Chartered Chemical Engineer and Six Sigma Black Belt, Loughbourough, UK.


A First Course in Probability and Markov Chains

Giuseppe Modica, Laura MPoggiolini

978-1-119-94487-4 / 1-119-94487-2

320 pp. Pub: 06/02/13


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

Markov Chain Monte Carlo (MCMC) methods are a subset of Monte Carlo methods that are applicable to a very wide range of problems. They are used to sample from complicated multivariate distributions that are not computable in practice and from which direct sampling (Monte Carlo) is not feasible

A First Course in Probability and Markov Chains presents an introduction to the basic elements in statistics and focuses in two main areas. The first part of the book looks at notions and structures in probability, including Combinatorics, probability measures, probability distributions, conditional probability, inclusion-exclusion formulas, random variables, dispersion indexes, independent random variables as well as Weak and Strong Laws of Large Numbers and Central Limit Theorem. A list of classical probability distributions, both discrete and continuous, is also included. In the second part of the book explores Discrete Time Discrete Markov Chains (DTDMC) which are discussed together with an introduction to Poisson processes and Continuous Time Discrete Markov Chains (CTDMC).

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

Under/graduate students in mathematics, science and engineering courses. Academics and students in mathematics, science and engineering courses.

Giuseppe Modica and Laura Poggiolini, Dipartimento di Sistemi e Informatica, Università di Firenze, Italy.

Optimal Redundancy Allocation: With Practical Statistical Applications and Theory

Igor A. Ushakov

978-1-118-38997-3 / 1-118-38997-2

256 pp. Pub: 08/04/13


With an overview of different approaches to optimal resource allocation, from classical Lagrange methods to modern algorithms based on ideas of evolution in biology, this book details the applied methods of optimisation, describes various methods of optimal redundancy problem solutions, and demonstrates how they can be solved with numerical examples and statistical methods. Blends probability and reliability theory, mathematical modeling, and mathematical statistics to solve common real-world optimal reliability problems.

• Features numerous statistical methods and algorithms of optimal resource allocation as well as the needed mathematical background and numerical examples.

• Describes how optimisation is an important part of various fields, from communication and transportation to energy transmission and counter-terrorism protection.

• Presents practical thoughts, opinions, and judgments on real-world applications of reliability theory via numerous case studies in areas such as transportation and communication and solves practical problems using mathematical models and algorithms.

• Contains many illustrative numerical examples and explanatory figures throughout in addition to numerous exercises with solutions and explanations.

Graduate/PhD students; Professionals and researchers who seek a mathematical review of reliability models, optimisation, quality and productivity, and the relevant mathematical and statistical applications; as a reference for secondary educators; and academic and corporate libraries.

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


Probabilistic Reliability Models

Igor A. Ushakov

978-1-118-34183-4 / 1-118-34183-X

252 pp. Pub: 15/10/12


Featuring practical approaches to various reliability theory applications, this book helps readers to understand and properly utilize statistical methods and optimal resource allocation in their everyday work to solve engineering problems.

• Blends probability theory and mathematical statistics to solve common real-world reliability problems.

• Contains many illustrative numerical examples and explanatory figures throughout in addition to numerous exercises with solutions and explanations.

• Features detailed case studies related to real-world technical projects, including software failure avalanches; gas-pipelines with underground storage; and ICBM control systems.

• Includes expertise from the author's vast industry and lecture experience from the past 40-plus years as a reliability engineer.

• Presents practical thoughts, opinions, and judgments on reliability theory applications to solve practical engineering problems with special attention is paid to hidden obstacles that lead to serious mistakes in reliability analysis.

As a textbook for a course in applied probability at the upper-undergraduate and graduate levels for students majoring in statistics, mathematics, operations research, and engineering; as a resource for professionals and researchers working in industry who seek a mathematical review of reliability models and the relevant applications; as a reference for secondary educators; and academic and corporate libraries.

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

Statistics for Finance, Business and Economics

Handbook of Decision Analysis

Gregory S. Parnell

978-1-118-17313-8 / 1-118-17313-9

352 pp. Pub: 13/05/13

Statistics for Finance, Business & Economics

With a focus on the philosophy, knowledge, science, and art of decision analysis, this book provides a balanced treatment of soft skills (decision making insights and presentation techniques) and hard skills (decision analysis techniques and mathematics), integrates single and multi- objective decision analysis, and presents multiple qualitative and quantitative techniques for each key decision analysis task.

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

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

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

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

• Fills a void in the current decision analysis literature and is a useful resource for practitioners in diverse fields with varied educational backgrounds.

As a reference and/or refresher for academics and practitioners in decision science, operations research, business, management science, engineering, statistics, and mathematics; as a guide for professional decision analysis training courses for practitioners; and academic and corporate libraries.

Gregory S. Parnell, Professor, Department of Systems Engineering, US Military Academy, West Point, NY, is also Executive Principle Analyst, Innovative Decisions, Inc., a leading decision analysis firm. Terry Bresnick, CEO, Innovative Decisions, Inc. and President Innovative Decision Analysis, Inc. Steven N. Tanib Partner and Fellow of Strategic Designs Group, as well as lead instructor Modeling for Strategic Insight, Stanford University’s Center for Professional Development. Eric R. Johnson is Associate Director of Bristol-Myers Squibb.


Handbook of Financial Risk Management

Ngai Hang Chan, Hoi Ying Wong

978-0-470-64715-8 / 0-470-64715-9

320 pp. Pub: 13/05/13


This is the authoritative volume on risk management techniques and simulations as applied to financial engineering topics, theories, and statistical methodologies.

The scope of the content is wider - and deeper - than any known competitor, including topics such as volatility, fixed-income derivatives, LIBOR Market Models, risk measures, and over two-dozen recognised simulation models. Throughout the material is organised around different asset classes. Although the primary focus is simulations, a complete algorithm may combine simulations with other techniques such as calibration, optimisation, and/or tree-building. Readers see how these methods interact and complement each other.

• Extensive references to the literature are given for further study.

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

As a handy, but complete reference for practitioners in the fields of finance, business, applied statistics, econometrics, and engineering and for libraries in academic and corporate settings; as a supplement to courses on financial risk management and simulation in similar departments and business schools at the graduate and MBA level.

NgaiI Hang Chan is Professor of Statistics and Director of the Risk Management (RM) Science Program at The Chinese University of Hong Kong. He received his Ph.D. from the University of Maryland in 1985. He has published a book with Wiley on time series and their applications to finance. He is an Associate Editor for four journals and a referee for countless publications and organisations. Hoi-Ying Wong is an Assistant Professor in the RM Science Program at The Chinese University of Hong Kong. He received his Ph.D. from The Hong Kong University of Science and Technology in 2001. His areas of interest include data analysis, statistical computing, RM, and stochastic calculus.

Financial Risk Modelling and Portfolio Optimization with R

Bernhard Pfaff

978-0-470-97870-2 / 0-470-97870-8

350 pp. Pub: 30/01/13


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

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

Financial Risk Modelling and Portfolio Optimization with R:

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

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

• Explores portfolio risk concepts and optimisation with risk constraints.

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

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

• Introduces stylized facts, loss function and risk measures.

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

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

Practitioners in finance and portfolio optimization (inc. banking). Graduate and postgraduate students in finance, economics, risk management.

Bernhard Eugen Heinrich Pfaff, Director, Invesco Asset Management Deutschland GmbH, Germany.


Statistics for the Social Sciences

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

Regina Baker

978-0-470-74993-7 / 0-470-74993-8

448 pp. Pub: 25/03/13


Based on a successful ICPSR course, this book aims to present time series analysis to student and practitioners from a diverse set of backgrounds. The author assumes minimum mathematical background in order to provide an accessible and comprehensive approach to both the theory and practice of time series analysis. A wide range of topics are covered, including ARIMA probability models, Estimation and forecasting techniques, OLS and the Gauss-Markov assumptions, Intervention models and addresses newer methodologies such as GLS and ADL models, Vector Autoregression and Error correction models. It also introduces Pooled cross-section time series models and ARCH and GARCH models.

The book is designed to break difficult concepts into manageable pieces whilst providing extensive case studies and exercises. It uses Lag operator algebra throughout the book to provide better understanding of applied time series analysis.

Students and professional researchers following statistics and time series courses in the political sciences, public policy, sociology and economics.

Regina M. Baker, Department of Political Science, University of Oregon, has extensive experience in teaching a time series course for The Inter-University Consortium for Political and Social Research (ICPSR) summer program.

Research Methods for Postgraduates, 3e

Tony Greenfield

978-1-118-34146-9 / 1-118-34146-5

450 pp. Pub: 11/03/13


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

Postgraduates/research students in all subject areas of research methodology.

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

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