Books in the Wiley Series in Probability and Statistics series

Based on the proceedings of a conference on Influence Diagrams for Decision Analysis, Inference and Prediction held at the University of California at Berkeley in May of 1988, this is the first book devoted to the subject.

Presents a system of multivariate analysis techniques in cases where statistical data may be of different measurement levels such as nominal, ordinal or interval. It covers methods of studying the stability of these techniques, including resampling by the bootstrap and jackknife and discusses sensitivity analysis through first-order approximations.

An exposition of fractals, shape and form, and point processes, which analyzes the current theoretical information. The organization of the text is such that each part can be read independently. Many case studies and true examples are used to illustrate the text.

A number of eminent experts on Clinical Trials, Epidemiology, Survival Analysis, and Genomics/Proteomics have contributed 30 carefully prepared and peer-reviewed articles to this book. Within the four sections, the articles have been organized so as to make the thematic transition between them as smooth as possible.

Recent books in the Wiley Series in Probability and Statistics Editors Vic Barnett J. Stuart Hunter David W. Scott Geoffrey S. Watson Ralph A. Bradley Joseph B. Kadane Adrian F.M. Smith Nicholas I. Fisher David G. Kendall Jozef L.

Pioneered by David Kendall, the statistical theory of shape is an emerging area generating considerable interest for statisticians, engineers, and computer scientists. Co--written by Dr. Kendall, this volume presents a coherent theory of shape developed from Kendalla s own approach known as static and kinematic theory.

"Hajek was undoubtedly a statistician of enormous power who, in his relatively short life, contributed fundamental results over a wide range of topics. " V. Barnett, University of Nottingham. Hajeka s writings in statistics are not only seminal but form a powerful unified body of theory.

Statistical inference is the process of drawing conclusions based upon the available data on the measurement of uncertainty of a defined event. It allows one to draw a conclusion or a generalization from the available data. , i.e. if there is smoke there is a good probability there is a fire.

This volume describes the algebra of matrices and shows how to apply them to today's problems in applied economics. The first section covers the essentials of matrices while the second section concentrates on major topics in applied economics such as regression, linear programming, and time series.

Focuses on latent class analysis (LCA) and latent transition analysis (LTA) with a comprehensive treatment of longitudinal latent class models. This book includes examples that enable the reader to acquire a conceptual and technical understanding and to apply techniques to address empirical research questions.

Maintaining the same nontechnical approach as its acclaimed predecessor, this second edition of Generalized Linear Models is now thoroughly extended to include the latest developments in the field, the most relevant computational approaches, and the most relevant examples from the fields of engineering and physical sciences.

Bayesian networks have found application in a number of fields, including risk analysis, consumer help desks, tissue pathology, pattern recognition, credit assessment, computer network diagnosis, and artificial intelligence. Bayesian Networks is a self-contained introduction to the theory and applications of Bayesian networks.

Random Data provides first-rate, practical tools for dynamic data and statistical methods for engineering problems. This revised bestseller presents the latest developed procedures and a complete rewrite of the Fast Fourier Transforms of applied fields. Plus, this resource includes a new chapter on frequency domain techniques.

Statistical science s first coordinated manual of methods for analyzing ordered categorical data, now fully revised and updated, continues to present applications and case studies in fields as diverse as sociology, public health, ecology, marketing, and pharmacy.

Approaches the analysis of variance (ANOVA) from an exploratory viewpoint while retaining customary least squares fitting methods. The authors emphasize both individual observations and the separate sections that ANOVA analysis produces.

Classification rules can be defined as objective, formal methods used for statistical classification. This text presents the central issues and placing particular emphasis on comparison, assessment and how to match method to application.

Written by well-known, award-winning authors, this is the first book to focus on high-dimensional data analysis while presenting real-world applications and research material.

Complex multivariate testing problems are frequently encountered in many scientific disciplines, such as engineering, medicine and the social sciences. As a result, modern statistics needs permutation testing for complex data with low sample size and many variables, especially in observational studies.

Praise for the First Edition "... the book is a valuable addition to the literature in the field, serving as a much-needed guide for both clinicians and advanced students.

In the spatial or spatio-temporal context, specifying the correct covariance function is fundamental to obtain efficient predictions, and to understand the underlying physical process of interest. This book focuses on covariance and variogram functions, their role in prediction, and appropriate choice of these functions in applications.

* This book lays out in clear detail the various but subtle threats (commonly called biases) to various validities of statistical research. * There is a comprehensive discussion of the sources of bias in comparative studies (both randomized and observational) and how to address them.

Upgraded to reflect the latest research and software applications on the topic, this new edition continues to provide a comprehensive introduction to the statistical methods for analyzing survival data.

This introduction to elementary probability theory is intended to serve as a pocketbook for applied statisticians. Topics include random walks; the principle of reflection; the probabilistic aspects of records; the geometric distribution; optimization and others.

Emphasizes the strategy of experimentation, data analysis, and the interpretation of experimental results. * Features numerous examples using actual engineering and scientific studies. * Presents statistics as an integral component of experimentation from the planning stage to the presentation of the conclusions.

An accessible guide to the multivariate time series tools used in numerous real-world applications Multivariate Time Series Analysis: With R and Financial Applications is the much anticipated sequel coming from one of the most influential and prominent experts on the topic of time series.

While most books on reliability deal with a description of component and system states as binary, i.e. , functioning or failed, many systems are composed of multistate components with different performance levels and several failure modes. This book addresses the need in a number of applications for a more refined description of these states.

This new edition answers the need for a comprehensive, cutting-edge overview of this important and emerging field effectively outlining all phases of this revolutionary analytical technique, from preprocessing to the analysis stage.

A comprehensive overview of Monte Carlo simulation that explores the latest topics, techniques, and real-world applications More and more of today s numerical problems found in engineering and finance are solved through Monte Carlo methods.