Erricos John Kontoghiorghes University of London, UK

Handbook of Parallel Computing and Statistics

Series: Statistics: A Series of Textbooks and Monographs Volume: 184

ISBN: 082474067X
Publication Date: 12/7/2005
Number of Pages: 544

Presents recent developments and research results in parallel processing
Addresses the design, analysis, and implementation of parallel algorithms for solving statistical problems
Promotes research at the interface of the disciplines of parallel computing and statistics
Discusses theoretical and practical issues associated with parallel statistical algorithms and the impact of parallelism on statistics
Considers specific applications involving parallelism and statistics

Presenting recent developments and results, the Handbook of Parallel Computing and Statistics promotes research at the interface of various disciplines. This book addresses the design, analysis, and implementation of parallel algorithms for solving statistical problems. It discusses theoretical and practical issues associated with parallel statistical algorithms, the impact of parallelism on statistics, and specific applications involving parallelism and statistics. With an introduction to the fundamentals of parallel computing, this text provides useful information for statisticians, econometricians, computer scientists, and numerical analysts as well as students and researchers.

Table of Contents
General-Parallel Computing. Optimization. Statistical Applications.

Daryl S Paulson BioSciences Laboratories, Inc., Bozeman, Montana, USA

Handbook of Regression and Modeling:
Applications for the Clinical and Pharmaceutical Industries

Series: Biostatistics Volume: 16

ISBN: 157444610X
Publication Date: 2/3/2006
Number of Pages: 500

Offers essential information for applying biostatistics to clinical and pharmaceutical research
Presents exploratory data analysis to evaluate the fit of the model to the actual data
Provides a well-defined procedure for adding or subtracting independent variables to the model variables
Applies statistical forecasting methods for when data are serially correlated
Includes methods that rely on computer software

Addressing a variety of topics related to this advanced aspect of statistics, Handbook of Regression Analysis and Modeling: Applications provides essential information for researchers, graduate students, and scientists who use regression analysis for biostatistics related to clinical and pharmaceutical research. This text includes exploratory data analysis to evaluate the fit of the model to the actual data. It presents a well-defined procedure for adding or subtracting independent variables to the model variables, with methods that rely on computer software. It also applies statistical forecasting methods when data are serially correlated. Additional material covers simple linear and multiple regressions.

Table of Contents

Basic Statistical Concepts. Simple Linear Regression. Special Problems in Simple Linear Regression. Some Aspects and Examples in Constructing a Valid Simple Regression Study. Multiple Linear Regression. Correlation in Multiple Regression. Issues in Multiple Linear Regression. Polynomial Regression. Special Topics in Multiple Regression. Indicator (Dummy) Variable Regression. Model Building/Model Selection. Analysis of Covariance. Logistic Regression.

Doug Ensley, J. Winston Crawley

Discrete Mathematics

ISBN: 0-471-47602-1
Hardcover
691 pages
September 2005

Description

Did you know that games and puzzles have given birth to many of today's deepest mathematical subjects? Now, with Douglas Ensley and Winston Crawley's Introduction to Discrete Mathematics, you can explore mathematical writing, abstract structures, counting, discrete probability, and graph theory, through games, puzzles, patterns, magic tricks, and real-world problems. You will discover how new mathematical topics can be applied to everyday situations, learn how to work with proofs, and develop your problem-solving skills along the way.

Online applications help improve your mathematical reasoning.

Highly intriguing, interactive Flash-based applications illustrate key mathematical concepts and help you develop your ability to reason mathematically, solve problems, and work with proofs. Explore More icons in the text direct you to online activities at www.wiley.com/college/ensley.

Improve your grade with the Student Solutions Manual.

A supplementary Student Solutions Manual contains more detailed solutions to selected exercises in the text.


Table of Contents

John Geweke

Contemporary Bayesian Econometrics and Statistics

ISBN: 0-471-67932-1
Hardcover
300 pages
September 2005

Tools to improve decision making in an imperfect world
This publication provides readers with a thorough understanding of Bayesian analysis that is grounded in the theory of inference and optimal decision making. Contemporary Bayesian Econometrics and Statistics provides readers with state-of-the-art simulation methods and models that are used to solve complex real-world problems. Armed with a strong foundation in both theory and practical problem-solving tools, readers discover how to optimize decision making when faced with problems that involve limited or imperfect data.

The book begins by examining the theoretical and mathematical foundations of Bayesian statistics to help readers understand how and why it is used in problem solving. The author then describes how modern simulation methods make Bayesian approaches practical using widely available mathematical applications software. In addition, the author details how models can be applied to specific problems, including:

Linear models and policy choices
Modeling with latent variables and missing data
Time series models and prediction
Comparison and evaluation of models
The publication has been developed and fine- tuned through a decade of classroom experience, and readers will find the author's approach very engaging and accessible. There are nearly 200 examples and exercises to help readers see how effective use of Bayesian statistics enables them to make optimal decisions. MATLABR and R computer programs are integrated throughout the book. An accompanying Web site provides readers with computer code for many examples and datasets.

This publication is tailored for research professionals who use econometrics and similar statistical methods in their work. With its emphasis on practical problem solving and extensive use of examples and exercises, this is also an excellent textbook for graduate-level students in a broad range of fields, including economics, statistics, the social sciences, business, and public policy.

Table of Contents

David Lucy

Introductory Statistics for Forensic Scientists

ISBN: 0-470-02201-9 Paperback
ISBN: 0-470-02200-0 Hardcover
272 pages
December 2005

Description

Introduction to Statistics for Forensic Scientists is an essential introduction to the subject, gently guiding the reader through the key statistical techniques used to evaluate various types of forensic evidence. Assuming only a modest mathematical background, the book uses real-life examples from the forensic science literature and forensic case-work to illustrate relevant statistical concepts and methods.
Opening with a brief overview of the history and use of statistics within forensic science, the text then goes on to introduce statistical techniques commonly used to examine data obtained during laboratory experiments. There is a strong emphasis on the evaluation of scientific observation as evidence and modern Bayesian approaches to interpreting forensic data for the courts. The analysis of key forms of evidence are discussed throughout with a particular focus on DNA, fibres and glass.

An invaluable introduction to the statistical interpretation of forensic evidence; this book will be invaluable for all undergraduates taking courses in forensic science.

Introduction to the key statistical techniques used in the evaluation of forensic evidence
Includes end of chapter exercises to enhance student understanding
Numerous examples taken from forensic science to put the subject into context

Table of Contents

Peter Rossi, Greg Allenby, Rob McCulloch

Bayesian Statistics and Marketing

ISBN: 0-470-86367-6
Hardcover
312 pages
December 2005

Description

The past decade has seen a dramatic increase in the use of Bayesian methods in marketing due, in part, to computational and modelling breakthroughs, making its implementation ideal for many marketing problems. Bayesian analyses can now be conducted over a wide range of marketing problems, from new product introduction to pricing, and with a wide variety of different data sources.
Bayesian Statistics and Marketing describes the basic advantages of the Bayesian approach, detailing the nature of the computational revolution. Examples contained include household and consumer panel data on product purchases and survey data, demand models based on micro-economic theory and random effect models used to pool data among respondents. The book also discusses the theory and practical use of MCMC methods.

Written by the leading experts in the field, this unique book:

Presents a unified treatment of Bayesian methods in marketing, with common notation and algorithms for estimating the models.
Provides a self-contained introduction to Bayesian methods.
Includes case studies drawn from the authors’ recent research to illustrate how Bayesian methods can be extended to apply to many important marketing problems.
Is accompanied by an R package, bayesm, which implements all of the models and methods in the book and includes many datasets. In addition the book’s website hosts datasets and R code for the case studies.
Bayesian Statistics and Marketing provides a platform for researchers in marketing to analyse their data with state-of-the-art methods and develop new models of consumer behaviour. It provides a unified reference for cutting-edge marketing researchers, as well as an invaluable guide to this growing area for both graduate students and professors, alike.
Table of Contents