Books in the Wiley Series in Probability and Statistics series

Bayesian analysis is a highly effective tool in the many cases when economic decisions are based on limited or imperfect information. For students and professionals familiar with basic econometrics, this volume is an accessible entry point into the Bayesian method.

Robust Statistics fills the need for a solid, up-to-date text that presents a broad overview of the theory of robust statistics, integrated with applications and computing. The book features in-depth coverage of the key methodology, including regression, multivariate analysis, and time series.

This text presents and describes methods for analysis of longitudinal data, with a strong emphasis on application of these methods to problems in the biomedical and behavioral sciences. Applied Longitudinal Data Analysis is geared more toward users, and not developers, of statistics.

Simple graphs have always played a useful role in the analysis and presentation of data. Advances in computing have created the potential to substantially expand the role of visualization in statistical analyses. This book is about ideas for the graphical analysis of regression data.

Describes modeling multiple-state time-to-event data using the innovative methodology of flowgraphing. This book aims to present material that utilizes real-world problems to encourage practical techniques for analyzing data from stochastic processes.

This book addresses the systematic analysis, modeling, and synthesis of integrated fractal and fractal-rate point processes in a rigorous but useful manner. Fractal and fractal-rate point processes exhibit both the scaling properties of fractals, and the discrete nature of random point processes.

Addresses connections and differences between jump regression analysis and image processing, with the goal of establishing better communication among research groups in the two areas of statistics. This book discusses procedures that are easy to use, simple to compute, and have proven statistical theory behind them.

A comprehensive overview of experimental design at the advanced level The development and introduction of new experimental designs in the last fifty years has been quite staggering and was brought about largely by an ever-widening field of applications.

This book provides readers with an elementary and comprehensive discussion on extreme value and related models. By using a large number of practical data from different science and engineering disciplines, it illustrates the practical importance and usefulness of extreme value modeling.

A highly detailed, yet readable treatment of the growing field of robust statistics--the statistics of approximate parametric models. Introducing concepts, theory, and applications, this work is designed to be accessible to a broad audience, avoiding allusions to high-powered mathematics while emphasizing ideas, heuristics, and background.

This book presents a complete, but fundamental and readily accessible treatment of nonparametric regression, a subset of the larger area of nonparametric statistics. The explanations are presented in a user-friendly format and along with S-Plus and R subroutines in an effort to derive many of the real-world data and results.

This volume covers the first full year of experimental design topics at the beginning graduate level. It attempts to present a well-balanced, down-to-earth, but complete coverage of the subject matter, with equal emphasis on both design and analysis. The mathematical rigor is strictly kept to a minimum.

Statistical Methods in Spatial Epidemiology, Second Edition describes, quantifies, and explains geographical variations in disease. The second edition has been updated and expanded in light of the events of September 11, 2001, and increasing concern over potential bioterrorism attacks.

This book is a comprehensive guide to simulation methods with explicit recommendations of methods and algorithms. It covers both the technical aspects of the subject, such as the generation of random numbers, non-uniform random variates and stochastic processes, and the use of simulation.

During the last decades long-memory processes have evolved as a vital and important part of time series analysis. This book attempts to give an overview of the theory and methods developed to deal with long-range dependent data as well as describe some applications of these methodologies to real-life time series.

The data analytic methods routinely applied in the analysis of recidivism data in criminology have been expanded to include immune individuals. This book describes the theory and methods of incorporating them.

Incorporates the many tools needed for modeling and pricing in finance and insurance Introductory Stochastic Analysis for Finance and Insurance introduces readers to the topics needed to master and use basic stochastic analysis techniques for mathematical finance.

With the growth of such fields as financial economics, so has the need for a thorough discussion of statistical size distributions.

This broadly based graduate--level textbook covers the major models and statistical tools currently used in the practice of econometrics. It examines the classical, the decision theory, and the Bayesian approaches, and contains material on single equation and simultaneous equation econometric models.

Inference and Prediction in Large Dimensions offers a predominantly theoretical coverage of statistical prediction, with some potential applications discussed, when data and/or parameters belong to a large or infinite dimensional space.

This book introduces the general philosophy of a number of unique topics, including response surface methodology and details least squares for response surface work; factorial designs at two levels; fitting second-order models; adequacy of estimation and the use of transformation; and occurrence and elucidation of ridge systems.

A balanced presentation of the theoretical, practical, and computational aspects of nonlinear regression. This book provides background material on linear regression, including a geometrical development for linear and nonlinear least squares.

In this volume, Srivastava examines both random variables that can be quantitatively measured as well as the latest multivariate methods. Most of the methods presented assume that the data has a normal distribution and that there are no outliers.

Meta Analysis: A Guide to Calibrating and Combining Statistical Evidence acts as a source of basic methods for scientists wanting to combine evidence from different experiments. The authors aim to promote a deeper understanding of the notion of statistical evidence. The book is comprised of two parts - The Handbook, and The Theory.

An expert introduction to stage-wise adaptive designs in all areas of statistics Stage-Wise Adaptive Designs presents the theory and methodology of stage-wise adaptive design across various areas of study within the field of statistics, from sampling surveys and time series analysis to generalized linear models and decision theory.

Praise for the First Edition "This impressive and eminently readable text... [is] a welcome addition to the statistical literature. " -The Indian Journal of Statistics Revised to reflect the current developments on the topic, Linear Statistical Models, Second Edition provides an up-to-date approach to various statistical model concepts.

Offers a rich collection of techniques. Discusses the foundational aspects and modern day practice. Accessible to anyone with knowledge of advanced calculus. Provides a unified framework to discuss the many different perspectives. Includes numerous practical applications in biostatistics, computer science, engineering and economics. .