Böcker i Wiley Series in Probability and Statistics-serien

An accessible, clearly organized survey of the basic topics of measure theory for students and researchers in mathematics, statistics, and physics In order to fully understand and appreciate advanced probability, analysis, and advanced mathematical statistics, a rudimentary knowledge of measure theory and like subjects must first be obtained.

This Set Contains:Continuous Multivariate Distributions, Volume 1, Models and Applications, 2nd Edition by Samuel Kotz, N. Balakrishnan and Normal L. Johnson; Continuous Univariate Distributions, Volume 1, 2nd Edition by Samuel Kotz, N. Balakrishnan and Normal L. Johnson; Continuous Univariate Distributions, Volume 2, 2nd Edition by Samuel Kotz, N.

This volume presents a detailed description of the statistical distributions that are commonly applied to such fields as engineering, business, economics and the behavioural, biological and environmental sciences.

Available in paperback for the first time, this bestseller provides a systematic account of the subject area, while concentrating on the most recent advances in the field. While the focus is on practical considerations, both theoretical and practical issues are explored.

Presents the first comprehensive guide to the analysis of spatial data. Each chapter covers a particular data format and the associated class of problems, introducing theory, giving computational suggestions, and providing examples. Methods are illustrated by computer-drawn figures.

This volume is written on the subject of the summarizing of the variability of statistical data known as the analysis of variance table. Penned in a readable style, it provides an up-to-date treatment of research in the area.

Dealing with the "how to do it" as well as the '"why it works," this book is designed for practitioners of principal component analysis. It explores topics such as: extension to p variables, scaling input data, inferential procedures, operations with group data, and vector interpretation.

A unique approach illustrating discrete distribution theory through combinatorial methods This book provides a unique approach by presenting combinatorial methods in tandem with discrete distribution theory. This method, particular to discreteness, allows readers to gain a deeper understanding of theory by using applications to solve problems.

Bayesian Statistical Modelling, Second Edition, provides an accessible approach to Bayesian computing and data analysis, with an emphasis on the interpretation of real data sets.

This valuable book-length treatment of the field offers coverage of estimation for situations where the model variables are observed subject to measurement error. Included are regression models with errors in the variables, latent variable models, and factor models.

This book provides a lucid and comprehensive introduction to simulation methods, and features examples and applications using Maple. Maple is widely used at undergraduate mathematics level in the UK, US and Europe. It is readily available to students, researchers and practitioners, and the code is easily transferrable into other languages.

WILEY-INTERSCIENCE PAPERBACK SERIES The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation.

This newly available and affordably priced paperback version of Exploring Data Tables, Trends, and Shapes presents major advances in exploratory data analysis and robust regression methods and explains the techniques, relating them to classical methods.

Discover how to optimize business strategies from both qualitative and quantitative points of view Operational Risk: Modeling Analytics is organized around the principle that the analysis of operational risk consists, in part, of the collection of data and the building of mathematical models to describe risk.

Asymptotic distribution theorems in probability and statistics have, from the beginning, depended on the classical theory of weak convergence of distribution functions in Euclidean space. The past several decades have seen the creation and extensive application of a more inclusive theory of weak convergence of probability measures on metric spaces.

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.