Series:

Wiley Series in Probability and Statistics

9 primary works • 22 total works

Book 1

Cover of Continuous Multivariate Distributions
This authoritative treatment of continuous multivariate distributions (CMD) focuses on the many ways in which various statistical distributions have been constructed, investigated, and applied over the past thirty-plus years. With more than 300 updated references and additional software algorithms, this comprehensive Third Edition of what is widely recognized as the definitive work on statistical distributions, includes a unique collection that encompasses continuous multivariate distributions, discrete multivariate distributions, continuous univariate distributions, and univariate discrete distributions.

Book 3

Cover of Continuous Univariate Distributions
The first volume in what is widely recognized as the definitive work on statistical distributions, this book is a comprehensive revision of Johnson and Kotz's acclaimed 1994 volume. It represents the next installment in a unique collection that encompasses continuous univariate distributions, discrete multivariate distributions, continuous multivariate distributions, and univariate discrete distributions. Presenting a comprehensive, authoritative, up-to-date treatment of continuous univariate distributions (CUD), this work focuses on the many ways in which various statistical distributions have been constructed, investigated, and applied over the past thirty-plus years.

Book 327

Cover of Sensitivity Analysis in Linear Regression
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. The text assesses the effect of measurement errors on the estimated coefficients, which is not accounted for in a standard least squares estimate, but is important where regression coefficients are used to apportion effects due to different variables. The robustness of the regression fit is assessed qualitatively and numerically.

Book 334

Cover of Continuous Multivariate Distributions, Volume 1
Continuous Multivariate Distributions, Volume 1, Second Edition provides a remarkably comprehensive, self-contained resource for this critical statistical area. It covers all significant advances that have occurred in the field over the past quarter century in the theory, methodology, inferential procedures, computational and simulational aspects, and applications of continuous multivariate distributions. In-depth coverage includes MV systems of distributions, MV normal, MV exponential, MV extreme value, MV beta, MV gamma, MV logistic, MV Liouville, and MV Pareto distributions, as well as MV natural exponential families, which have grown immensely since the 1970s. Each distribution is presented in its own chapter along with descriptions of real-world applications gleaned from the current literature on continuous multivariate distributions and their applications.

Book 444

Cover of Univariate Discrete Distrbutions 3e
This book helps you discover the latest advances in discrete distributions theory. This third edition of the critically acclaimed "Univariate Discrete Distributions" provides a self-contained, systematic treatment of the theory, derivation, and application of probability distributions for count data. Generalized zeta-function and q-series distributions have been added and are covered in detail. New families of distributions, including Lagrangian-type distributions, are integrated into this thoroughly revised and updated text. Additional applications of univariate discrete distributions are explored to demonstrate the flexibility of this powerful method. A thorough survey of recent statistical literature draws attention to many new distributions and results for the classical distributions. Approximately 450 new references along with several new sections are introduced to reflect the current literature and knowledge of discrete distributions.
Beginning with mathematical, probability, and statistical fundamentals, the authors provide clear coverage of the key topics in the field, and includes: Families of discrete distributions; Binomial distribution; Poisson distribution; Negative binomial distribution; Hypergeometric distributions; Logarithmic and Lagrangian distributions; Mixture distributions; Stopped-sum distributions; Matching, occupancy, runs, and q-series distributions; and Parametric regression models and miscellaneas. Emphasis continues to be placed on the increasing relevance of Bayesian inference to discrete distribution, especially with regard to the binomial and Poisson distributions. New derivations of discrete distributions via stochastic processes and random walks are introduced without unnecessarily complex discussions of stochastic processes. Throughout the third edition, extensive information has been added to reflect the new role of computer-based applications. With its thorough coverage and balanced presentation of theory and application, this is an excellent and essential reference for statisticians and mathematicians.

Book 472

Cover of Precedence-Type Tests and Applications
Full coverage of statistical techniques for developing and implementing precedence-type tests Precedence-Type Tests and Applications provides a comprehensive overview of theoretical and applied approaches to a variety of problems in which precedence-type test procedures can be used. The authors clearly demonstrate the effectiveness of these tests in life-testing situations designed for making quick and reliable decisions in the early stages of an experiment. Most of the text's examples use life-time data; however, theoretical properties are also discussed in the context of precedence testing. Monte Carlo studies are used to illustrate important results. Following the authors' careful step-by-step instructions and guidance, readers master the wide range of statistical techniques involved in the development and implementation of precedence-type tests. The book covers the foundations of precedence testing research from the early 1960s up to the most recent theory and applications, including the authors' current contributions to the field.
The book features the following parts: Part A deals with the original precedence test and some properties of precedence and related test procedures Part B explores alternatives to precedence testing, including maximal precedence, weighted forms of precedence and maximal precedence, and Wilcoxon-type rank-sum precedence tests and their properties Part C compares the extension of precedence, maximal precedence, and Wilcoxon-type rank-sum precedence tests to situations in which the sample arising from the life-testing experiment is progressively Type-II censored Part D examines precedence-type tests in multi-sample situations and selection problems Tables are presented throughout the book to facilitate the application of the tests to practical problems. Helpful examples illustrate all of the precedence-type procedures, and an extensive bibliography enables readers to explore specialized topics in greater depth. This book is a recommended reference for researchers and practitioners in reliability and life-time data analysis, applied probabilists, and engineers. It also serves as a supplemental text for courses in nonparametric statistics and reliability.

Book 728

Cover of Statistical Size Distributions in Economics and Actuarial Sciences
A comprehensive account of economic size distributions around the world and throughout the years In the course of the past 100 years, economists and applied statisticians have developed a remarkably diverse variety of income distribution models, yet no single resource convincingly accounts for all of these models, analyzing their strengths and weaknesses, similarities and differences. Statistical Size Distributions in Economics and Actuarial Sciences is the first collection to systematically investigate a wide variety of parametric models that deal with income, wealth, and related notions. Christian Kleiber and Samuel Kotz survey, compliment, compare, and unify all of the disparate models of income distribution, highlighting at times a lack of coordination between them that can result in unnecessary duplication. Considering models from eight languages and all continents, the authors discuss the social and economic implications of each as well as distributions of size of loss in actuarial applications.
Specific models covered include:* Pareto distributions* Lognormal distributions* Gamma-type size distributions* Beta-type size distributions* Miscellaneous size distributions Three appendices provide brief biographies of some of the leading players along with the basic properties of each of the distributions. Actuaries, economists, market researchers, social scientists, and physicists interested in econophysics will find Statistical Size Distributions in Economics and Actuarial Sciences to be a truly one-of-a-kind addition to the professional literature.

Book 764

Cover of Runs and Scans with Applications
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. Runs and Scans with Applications presents new and exciting content in a systematic and cohesive way in a single comprehensive volume, complete with relevant approximations and explanations of some limit theorems. The authors provide detailed discussions of both classical and current problems, such as: Sooner and later waiting time Consecutive systems Start-up demonstration testing in life-testing experiments Learning and memory models "Match" in genetic codes Runs and Scans with Applications offers broad coverage of the subject in the context of reliability and life-testing settings and serves as an authoritative reference for students and professionals alike.

Book 768

Cover of Records

Records

by Barry C. Arnold, N. Balakrishnan, and Haikady N. Nagaraja

Published 22 September 1998
The first and only comprehensive guide to modern record theory and its applications Although it is often thought of as a special topic in order statistics, records form a unique area, independent of the study of sample extremes. Interest in records has increased steadily over the years since Chandler formulated the theory of records in 1952. Numerous applications of them have been developed in such far-flung fields as meteorology, sports analysis, hydrology, and stock market analysis, to name just a few. And the literature on the subject currently comprises papers and journal articles numbering in the hundreds. Which is why it is so nice to have this book devoted exclusively to this lively area of statistics. Written by an exceptionally well-qualified author team, Records presents a comprehensive treatment of record theory and its applications in a variety of disciplines. With the help of a multitude of fascinating examples, Professors Arnold, Balakrishnan, and Nagaraja help readers quickly master basic and advanced record value concepts and procedures, from the classical record value model to random and multivariate record models.
The book follows a rational textbook format, featuring witty and insightful chapter introductions that help smooth transitions from one topic to another and challenging chapter-end exercises, which expand on the material covered. An extensive bibliography and numerous references throughout the text specify sources for further readings on relevant topics. Records is a valuable professional resource for probabilists and statisticians, in addition to applied statisticians, meteorologists, hydrologists, market analysts, and sports analysts. It also makes an excellent primary text for courses in record theory and a supplement to order statistics courses.

Cover of Discrete Multivariate Distributions
Timely, comprehensive, practical--an important working resource for all who use this critical statistical method Discrete Multivariate Distributions is the only comprehensive, single-source reference for this increasingly important statistical subdiscipline. It covers all significant advances that have occurred in the field over the past quarter century in the theory, methodology, computational procedures, and applications of discrete multivariate distributions in a wide range of disciplines. Distributions covered include multinomial, binomial, negative binomial, Poisson, power series, hypergeometric, Polya-Eggenberger, Ewens, orders, and some families of distributions. Each distribution is presented in its own chapter, along with necessary details and descriptions of real-world applications gleaned from the current literature on discrete multivariate distributions. Discrete Multivariate Distributions is the fourth volume of the ongoing revision of Johnson and Kotz's acclaimed Distributions in Statistics--universally acknowledged to be the definitive work on statistical distributions.
Originally planned as a revision of Chapter 11 of that classic, this project soon blossomed into a substantial volume as a result of the unprecedented growth that has occurred in the literature on discrete multivariate distributions and their applications over the past quarter century. The only comprehensive, single-volume work on the subject, this valuable reference affords statisticians direct access to all of the latest developments concerning discrete multivariate distributions. Concentrating primarily on areas of interest to theoretical as well as applied statisticians, the authors provide complete coverage of several important discrete multivariate distributions. These include multinomial, binomial, negative binomial, Poisson, power series, hypergeometric, Polya-Eggenberger, Ewens, orders, and some families of distributions. Discrete Multivariate Distributions begins with a general overview of the multivariate method in which the authors lay the basic theoretical groundwork for the discussions that follow.
For clarity and consistency, subsequent chapters follow a similar format, beginning with a concise historical account followed by a discussion of properties and characteristics. Coverage then advances to in-depth explorations of inferential issues and applications, liberally supplemented with helpful details and a collection of real-world applications obtained from the authors' extensive searches of current literature worldwide. Discrete Multivariate Distributions is an essential working resource for researchers, professionals, practitioners, and graduate students in statistics, mathematics, computer science, engineering, medicine, and the biological sciences.

Cover of Order Statistics 3e
This volume provides an up-to-date coverage of the theory and applications of ordered random variables and their functions. Furthermore, it develops the distribution theory of OS systematically. Applications include procedures for the treatment of outliers and other data analysis techniques. Even when chapter and section headings are the same as in OSII, there are appreciable changes, mostly additions, with some obvious deletions. Parts of old Ch. 7, for example, are prime candidates for omission. Appendices are designed to help collate tables, computer algorithms, and software, as well as to compile related monographs on the subject matter. Extensive exercise sets will continue, many of them replaced by newer ones.

Cover of Continuous Univariate Distributions 2e V 1
This monograph presents a detailed description of important statistical distributions that are commonly used in various applied areas such as engineering, business, economics and behavioural, biological and environmental sciences. It provides a detailed description of general and specific continuous distributions. These distributions are used in reliability and communication engineering, business and economics.

Cover of Continuous Univariate Distributions 2e V 2
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. The authors cover specific distributions, including logistic, slash, bathtub, F, non-central Chi-square, quadratic form, non-central F, non-central t, and other miscellaneous distributions.

Cover of Extreme Value and Related Models with Applications in Engineering and Science
A straightforward, practical guide to extreme value modeling for todaya s world Measuring and interpreting data for extreme values presents a unique and important challenge that has far--reaching implications for all aspects of modern engineering and science. Extreme Value and Related Models with Applications in Engineering and Science reflects the latest information in this growing field. The book incorporates illuminating real--world examples from such areas as structural engineering, hydraulics, meteorology, materials science, highway traffic analysis, environmetrics, and climatology, and is designed to help engineers, mathematicians, statisticians, and scientists gain a clearer understanding of extreme value theory and then translate that knowledge into practical applications within their own fields of research.
The book provides:* A unique focus on modern topics including data analysis and inference* Specific data in such areas as wind, flood, chain strength, electrical insulation, fatigue, precipitation, and wave heights* Useful techniques for addressing extreme value problems, including discrete, continuous, univariate, and multivariate models* Coverage of order statistics, return period, exceedances and shortfalls, along with detailed explanations on how to obtain exact distributions for these statistics* An in--depth look at asymptotic models and the limit distributions of maxima, minima, and other order statistics Enhanced with numerous graphs and exercises, plus an extensive bibliography for further study, this text is an important reference source for engineers designing structures that will withstand even the most extreme circumstances.

Cover of Handbook of Regression Analysis With Applications in R

Handbook and reference guide for students and practitioners of statistical regression-based analyses in R 

Handbook of Regression Analysis with Applications in R, Second Edition is a comprehensive and up-to-date guide to conducting complex regressions in the R statistical programming language. The authors’ thorough treatment of “classical” regression analysis in the first edition is complemented here by their discussion of more advanced topics including time-to-event survival data and longitudinal and clustered data.  

The book further pays particular attention to methods that have become prominent in the last few decades as increasingly large data sets have made new techniques and applications possible. These include: 

  • Regularization methods 
  • Smoothing methods 
  • Tree-based methods 

In the new edition of the Handbook, the data analyst’s toolkit is explored and expanded. Examples are drawn from a wide variety of real-life applications and data sets. All the utilized R code and data are available via an author-maintained website. 

Of interest to undergraduate and graduate students taking courses in statistics and regression, the Handbook of Regression Analysis will also be invaluable to practicing data scientists and statisticians. 

Cover of Introduction to Probability – Models and Applications
With a focus on models and tangible applications of probability from physics, computer science, and other related disciplines, this book successfully guides readers through fundamental coverage for enhanced understanding of the problems. Topical coverage includes: bivariate discrete random, continuous random, and stochastic independence-multivariate random variables; transformations of random variables; covariance-correlation; multivariate distributions; the Central Limit Theorem; stochastic processes; and more. The book is ideal for a second course in probability and for researchers and professionals.

Cover of Advanced Regression Model Building Techniques

Cover of Measuring Agreement

Presents statistical methodologies for analyzing common types of data from method comparison experiments and illustrates their applications through detailed case studies

Measuring Agreement: Models, Methods, and Applications features statistical evaluation of agreement between two or more methods of measurement of a variable with a primary focus on continuous data. The authors view the analysis of method comparison data as a two-step procedure where an adequate model for the data is found, and then inferential techniques are applied for appropriate functions of parameters of the model. The presentation is accessible to a wide audience and provides the necessary technical details and references. In addition, the authors present chapter-length explorations of data from paired measurements designs, repeated measurements designs, and multiple methods; data with covariates; and heteroscedastic, longitudinal, and categorical data. The book also:

- Strikes a balance between theory and applications

- Presents parametric as well as nonparametric methodologies

- Provides a concise introduction to Cohen's kappa coefficient and other measures of agreement for binary and categorical data

- Discusses sample size determination for trials on measuring agreement

- Contains real-world case studies and exercises throughout

- Provides a supplemental website containing the related datasets and R code

Measuring Agreement: Models, Methods, and Applications is a resource for statisticians and biostatisticians engaged in data analysis, consultancy, and methodological research. It is a reference for clinical chemists, ecologists, and biomedical and other scientists who deal with development and validation of measurement methods. This book can also serve as a graduate-level text for students in statistics and biostatistics.

Cover of Identification of Outliers in Large Statistical Data
* Up-to-date, state-of-the-art coverage * Website to include executable computer programs that will allow users to apply methods discussed in the book * Virtually non-mathematical language, readable to a wide range of audiences * Presents methods that are applicable to very large data sets * Examples drawn from various fields * Use of graphics * Applied focus

Cover of Survival Models and Data Analysis
Survival analysis deals with the distribution of life times, essentially the times from an initiating event such as birth or the start of a job to some terminal event such as death or pension. This book, originally published in 1980, surveys and analyzes methods that use survival measurements and concepts, and helps readers apply the appropriate method for a given situation. Four broad sections cover introductions to data, univariate survival function, multiple-failure data, and advanced topics.