Books in the Wiley Series in Probability and Statistics series

An accessible introduction to the most current thinking in and practicality of forecasting techniques in the context of time-oriented data. Analyzing time-oriented data and forecasting are among the most important problems that analysts face across many fields, ranging from finance and economics to production operations and the natural sciences.

A non--mathematical introductory statistics text that combines clear explanation of concepts, an extensive coverage of useful statistical techniques, and numerous illustrations with live data from diverse fields. Emphasizes assumptions and limitations of the statistical methods so that violations of assumptions can be avoided.

Applies the well-developed tools of the theory of weak convergence of probability measures to large deviation analysis--a consistent new approach The theory of large deviations, one of the most dynamic topics in probability today, studies rare events in stochastic systems.

Explains the concepts and use of univariate Box-Jenkins/ARIMA analysis and forecasting through 15 case studies. Cases show how to build good ARIMA models in a step-by-step manner using real data. Also includes examples of model misspecification. Provides guidance to alternative models and discusses reasons for choosing one over another.

This new material is concerned with the theory and applications of probability, statistics and analysis of canonical moments. It provides a powerful tool for the determination of optimal experimental designs, for the calculation of the main characteristics of random walks, and for other moment problems appearing in probability and statistics.

Statistical inference is used to draw conclusions about the general from the particular. This book provides an introduction to the central ideas and methods of statistical inference by integrating conceptual development with the analysis of data.

This text provides a fast survey of the field of probability and should be suitable for the reader with limited experience of the subject. The second edition contains exercises throughout.

Unlike other books on variance components, Statistical Tests for Mixed Linear Models continues beyond point estimation to cover hypothesis and data testing. By addressing these areas, the author presents practical applications of variance component models through testing of fixed effects and variance components.

Integrates methods and data based interpretations relevant to multidata analysis. This text includes enhanced computing power in technology, such as numerics and graphics, plus major statistical methodological developments stimulated by real-world problems and needs.

A stochastic process is any process governed by laws of probability, ranging from the genetic probability of having brown eyes, to the chances of a line of cars passing a specific highway point. This book provides information on the latest techniques and statistical Markov process theory used in the control of queuing systems (e.g.

A fascinating chronicle of the lives and achievements of the men and women who helped shapethe science of statistics This handsomely illustrated volume will make enthralling reading for scientists, mathematicians, and science history buffs alike.

An in-depth look at current issues, new research findings, and interdisciplinary exchange in survey methodology and processing Survey Measurement and Process Quality extends the marriage of traditional survey issues and continuous quality improvement further than any other contemporary volume.

A step-by-step guide for today's modeling and simulation practices This new guide for modeling and simulation of discrete-event systems (DES) demonstrates why simulation is fast becoming the method of choice for the evaluation of system performance in science, engineering, and management.

Introduces applied research areas and a number of real-life questions and examples with basic methods in nonparametric statistics, including the concept of censoring, which distinguishes survival analysis from other areas of statistics.

Other volumes in the Wiley Series in Probability and Mathematical Statistics, Ralph A. Bradley, J. Stuart Hunter, David G. Kendall, & Geoffrey S. Watson, Advisory Editors Statistical Models in Applied Science Karl V. Bury Of direct interest to engineers and applied scientists, this book presents general principles of statistics and specific distribution methods and models. Prominent distribution properties and methods that are useful over a wide range of applications are covered in detail. The strengths and weaknesses of the distributional models are fully described, giving the reader a firm, intuitive approach to the selection of the model most appropriate to the problem at hand. 1975 656 pp. Fitting Equations To Data Computer Analysis of Multifactor Data for Scientists and Engineers Cuthbert Daniel & Fred S. Wood With the assistance of John W. Gorman The purpose of this book is to help the serious data analyst, scientist, or engineer with a computer to: recognize the strengths and limitations of his data; test the assumptions implicit in the least squares methods used to fit the data; select appropriate forms of the variables; judge which combinations of variables are most influential; and state the conditions under which the fitted equations are applicable. Throughout, mathematics is kept at the level of college algebra. 1971 342 pp. Methods for Statistical Analysis of Reliability And Life Data Nancy R. Mann, Ray E. Schafer & Nozer D. Singpurwalla This book introduces failure models commonly used in reliability analysis, and presents the most useful methods for analyzing the life data of these models. Highlights include: material on accelerated life testing; a comprehensive treatment of estimation and hypothesis testing; a critical survey of methods for system-reliability confidence bonds; and methods for simulation of life data and for testing fit. 1974 564 pp.

The outgrowth of more than 40 years of experience teaching and consulting with students and active researchers in many disciplines, this is a useful guide for both students and active researchers to experimental design.

Takes a look at the application of random graphs to pattern recognition. This work presents examples of applications of the graphs studied, as well as the theoretical treatment of their properties; and a compilation of topics in discrete mathematics, pattern recognition, and machine learning.

An accessible introduction to the use of regression analysis in the social sciences Regression with Social Data: Modeling Continuous and Limited Response Variables represents the most complete and fully integrated coverage of regression modeling currently available for graduate-level behavioral science students and practitioners.

Expert practical and theoretical coverage of runs and scans This volume presents both theoretical and applied aspects of runs and scans, and illustrates their important role in reliability analysis through various applications from science and engineering.

This text uses simulation as a computational aid in dealing with and creating models of reality. Its main objective is to incorporate simulation as an integral part of the interaction between data and the models which may have approximately generated them.

In this volume, influential statistician Christopher Lloyd presents a comprehensive, self-contained discussion of the special statistical methods used to analyse and report count data, also known as categorical data. The emphasis here is on modern methods, with some attention paid to traditional methods as well.

Fractional factorial plans are of immense practical utility in many fields of investigation (particularly in experimental design, quality control, and quality improvment), and research in this area is progressing at a vigorous pace with an already voluminous and growing amount of research papers published.

The importance of nonparametric methods in modern statistics has grown dramatically since their inception in the mid-1930s. Requiring few or no assumptions about the populations from which data are obtained, they have emerged as the preferred methodology among statisticians and researchers performing data analysis.

Uniquely combining theory, application, and computing, this book explores the spectral approach to time series analysis The use of periodically correlated (or cyclostationary) processes has become increasingly popular in a range of research areas such as meteorology, climate, communications, economics, and machine diagnostics.

A unique, practical guide for industry professionals who need to improve product quality and reliability in repairable systems Owing to its vital role in product quality, reliability has been intensely studied in recent decades. Most of this research, however, addresses systems that are nonrepairable and therefore discarded upon failure.

Considers neoclassical models in light of results that can go wrong with them to bring about better models. This work offers an examination of the LTCM collapse.

Includes a chapter on multiple linear regression in biomedical research, with sections containing the multiple linear regressions model and least squares; the ANOVA table, parameter estimates, and confidence intervals; partial f-tests; polynomial regression; and analysis of covariance.

This text provides a mathematical foundation for prediction theory and time series analysis using the geometry of Hilbert spaces. Emphasis is on foundation and structure, supported by theory, application and exercises to provide reinforcement and to extend discussions.