Books in the Wiley Series in Probability and Statistics series

This third edition of the successful Elements of Applied Stochastic Processes improves on the last edition by condensing the material and organizing it into a more teachable format. With more in-depth coverage of Markov chains and simple Markov process, the authors provide added emphasis to statistical inference in stochastic processes.

This is a revision of the classic book Applied Discriminant Analysis by Carl Huberty. Dr. Huberty has taken on a co-author, Steve Olejnik, who helped to update existing material and write new chapters. New terms, updated discussion of topics that have recently become more important, new computer applications, and new references have been included.

Environmental Statistics provides a broad overview of the statistical methodology used in the study of the environment, written in an accessible style by a leading authority on the subject. * Provides broad coverage of the methodology used in the statistical investigation of environmental issues.

This work covers stochastic order relations, which provide insight into the behaviour of complex stochastic (random) systems and enables the user to collect comparative data. Application areas include queuing systems, actuarial and financial risk, decision making, and stochastic simulation.

A system for statistical computing and dynamic graphics based on the LISP language is described in this book, which shows how to use the system for statistical calculations and graphs. No prior knowledge of LISP is assumed, and examples are included.

Explores the application of bootstrap to problems that place unusual demands on the method. The bootstrap method, introduced by Bradley Efron in 1973, is a nonparametric technique for inferring the distribution of a statistic derived from a sample.

This study deals with the calculations of mathematical expectations, primarily by simulation methods. The authors explore the present state of research and signal the types of problems raised by new methods. Topics discussed include Monte Carlo methods and the simulation of stochastic processes.

This second edition covers new developments in the analysis of statistical time series since the 1st edition was published in 1976. There is a considerable expansion of material, including added discussion of central limit theorems, estimation and generalized least squares.

Combines recent developments in resampling technology (including the bootstrap) with new methods for multiple testing that are easy to use, convenient to report and widely applicable. Software from SAS Institute is available to execute many of the methods and programming is straightforward for other applications.

The first step-by-step guide to conducting successful Chi-squared tests Chi-squared testing is one of the most commonly applied statistical techniques. It provides reliable answers for researchers in a wide range of fields, including engineering, manufacturing, finance, agriculture, and medicine.

Theory of Preliminary Test and Stein-Type Estimation with Applications provides a com-prehensive account of the theory and methods of estimation in a variety of standard models used in applied statistical inference.

Of related interest. Nonlinear Regression Analysis and its Applications Douglas M. Bates and Donald G. Watts ". an extraordinary presentation of concepts and methods concerning the use and analysis of nonlinear regression models. highly recommend[ed].

The purpose of this work is to provide a unified treatment of both the theory and practice of factor analysis and latent variables models.

Developing new mathematical tools to study discrete event systems has been a major focus in the fields of systems theory and operations research. This study discusses the two lines of investigation in DES research that have emerged: logical/qualitative issues and temporal/quantitative analysis.

Explains how Hilbert space techniques cross the boundaries into the foundations of probability and statistics. Focuses on the theory of martingales stochastic integration, interpolation and density estimation. Includes a copious amount of problems and examples.

This study presents the concepts and practice of interpreting single equation dynamic regression models. Emphasis is placed on possible dynamic patterns, such as distributed lag responses of the output series to the input series and the auto-correlation patterns of the regression disturbance.

Introduces the basic principles and ideas of MACSYMA, a computer programming system designed to perform mathematical computations and manipulations in symbolic as well as numerical form.

In this well written book, the authors treat the fundamental question of response-adaptive randomization as dealing with the trade-off between minimizing the expected number of treatment failures and maximizing the power of inferential tests.

A fascinating investigation into the foundations of statistical inference This publication examines the distinct philosophical foundations of different statistical modes of parametric inference.

Visual statistics accomplishes the goal of bringing the most complex and advanced statistical methods within the reach of those with little statistical training by using animated graphics of the data. This text shows how to make dynamic visualizations tht are fully interactive and respond instantly to the user's nudges and prods.

A comprehensive text and reference bringing together advances in the theory of probability and statistics and relating them to applications.

This book devoted to sequential estimation presents the advances of the past fifteen years including those in the areas of three-stage accelerated sequential sampling procedures.

This volume treats linear regression diagnostics as a tool for the application of linear regression models to real-life data. The presentation makes extensive use of examples to illustrate theory.

This book provides state-of-the-art coverage for the researcher confronted with designing and executing a simulation study using continuous multivariate distributions. The concise writing style makes the book accessible to a wide audience.

Reflecting more than 30 years of teaching experience in the field, this guide provides engineers with an introduction to statistics and its applicability to engineering. Examples cover a wide range of engineering applications, including both chemical engineering and semiconductors.

This monograph uses Bayesian statistical methods to explain the nature and methodology of clinical trials.

An introduction to the theory and methods of robust statistics, which aims to illustrate the need for robust procedures in a variety of statistical contexts, and to develop the techniques and concepts useful in the analysis of new statistical models and procedures.

An overview of the survey method of statistical analysis, which explores three main areas of nonsampling survey error: non-response in obtaining data from sample members, problems with the sampling frame and inadequacies in the process of obtaining survey measures from respondents.

A balanced presentation of both theoretical and applied material with numerous problem sets to illustrate important concepts. Demonstrates the use of computers and calculators to facilitate problem solving, as well as numerous applications to illustrate basic theory.

- It reveals the interrelationships between multiple variables and features of the underlying conditional independence. - It covers conditional independence, several types of independence graphs, Gaussian models, issues in model selection, regression and decomposition. - Many numerical examples and exercises with solutions are included.