Bücher von N. Balakrishnan

This monograph offers a thorough and updated guide to the theory and methods of progressive censoring, an area that has experienced tremendous growth in recent years. Progressive censoring, originally proposed in the 1950s, is an efficient method of handling samples from industrial experiments involving lifetimes of units that have either failed or censored in a progressive fashion during the life test, with many practical applications to reliability and quality.Key topics and features:Data sets from the literature as well as newly simulated data sets are used to illustrate concepts throughout the textEmphasis on real-life applications to life testing, reliability, and quality controlDiscussion of parametric and nonparametric inferenceCoverage of experimental design with optimal progressive censoringThe Art of Progressive Censoring is a valuable reference for graduate students, researchers, and practitioners inapplied statistics, quality control, life testing, and reliability. With its accessible style and concrete examples, the work may also be used as a textbook in advanced undergraduate or beginning graduate courses on censoring or progressive censoring, as well as a supplementary textbook for a course on ordered data.

S. Panchapakesan has made significant contributions to ranking and selection and has published in many other areas of statistics, including order statistics, reliability theory, stochastic inequalities, and inference. Written in his honor, the twenty invited articles in this volume reflect recent advances in these fields and form a tribute to Panchapakesan's influence and impact on these areas. Thematically organized, the chapters cover a broad range of topics from:* Inference* Ranking and Selection* Multiple Comparisons and Tests * Agreement Assessment * Reliability * Biostatistics Featuring theory, methods, applications, and extensive bibliographies with special emphasis on recent literature, this comprehensive reference work will serve researchers, practitioners, and graduate students in the statistical and applied mathematics communities.

Enrique Castillo is a leading figure in several mathematical, statistical, and engineering fields, having contributed seminal work in such areas as statistical modeling, extreme value analysis, multivariate distribution theory, Bayesian networks, neural networks, functional equations, artificial intelligence, linear algebra, optimization methods, numerical methods, reliability engineering, as well as sensitivity analysis and its applications. Organized to honor Castillo's significant contributions, this volume is an outgrowth of the International Conference on Mathematical and Statistical Modeling and covers recent advances in the field. Also presented are applications to safety, reliability and life-testing, financial modeling, quality control, general inference, as well as neural networks and computational techniques.The book is divided into nine major sections:* Distribution Theory and Applications* Probability and Statistics* Order Statistics and Analysis* Engineering Modeling* Extreme Value Theory* Business and Economics Applications* Statistical Methods* Applied Mathematics* Discrete DistributionsThis comprehensive reference work will appeal to a diverse audience from the statistical, applied mathematics, engineering, and economics communities. Practitioners, researchers, and graduate students in mathematical and statistical modeling, optimization, and computing will benefit from this work.

Parametric and semiparametric models are tools with a wide range of applications to reliability, survival analysis, and quality of life. This self-contained volume examines these tools in survey articles written by experts currently working on the development and evaluation of models and methods. While a number of chapters deal with general theory, several explore more specific connections and recent results in "real-world" reliability theory, survival analysis, and related fields.

This book explains how mathematics can be utilized to provide efficient techniques for implementing effective coverage, deployment, transmission, data processing, signal processing, and data protection within distributed sensor networks.

Along with a review of general developments relating to bivariate distributions, this volume also covers copulas, a subject which has grown immensely in recent years. In addition, it examines conditionally specified distributions and skewed distributions.

Lognormal distributions are one of the most commonly studied models in the sta tistical literature while being most frequently used in the applied literature.

This book offers a thorough and updated guide to the theory and methods of progressive censoring, an area that has experienced tremendous growth over the last decade.

This new book offers a guide to the theory and methods of progressive censoring. In many industrial experiments involving lifetimes of machines or units, experiments have to be terminated early. Progressive Censoring first introduces progressive sampling foundations, and then discusses various properties of progressive samples.

This new book offers a guide to the theory and methods of progressive censoring. In many industrial experiments involving lifetimes of machines or units, experiments have to be terminated early. Progressive Censoring first introduces progressive sampling foundations, and then discusses various properties of progressive samples.

The conference was organized jointly by St. Petersburg State University, St. Petersburg branch of Mathematical Institute, and the Euler Institute, and was partially sponsored by the Russian Foundation of Basic Researches.