Wiley Theory of Preliminary Test and Stein-Type Estimation with Applications (Wiley Series in Probability and Statistics)

Review "…almost certainly the best single source for a detailed treatment of preliminary test estimators…" (Journal of the American Statistical Association, June 2007) "…the book is clearly written and [in addition to a textbook for students] could also be used as a reference for practitioners and researchers…" (Mathematical Reviews, 2007d) Product Description 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. It is an in-depth introduction to the estimation theory for graduate students, practitioners, and researchers in various fields, such as statistics, engineering, social sciences, and medical sciences. Coverage of the material is designed as a first step in improving the estimates before applying full Bayesian methodology, while problems at the end of each chapter enlarge the scope of the applications. This book contains clear and detailed coverage of basic terminology related to various topics, including: * Simple linear model; ANOVA; parallelism model; multiple regression model with non-stochastic and stochastic constraints; regression with autocorrelated errors; ridge regression; and multivariate and discrete data models * Normal, non-normal, and nonparametric theory of estimation * Bayes and empirical Bayes methods * R-estimation and U-statistics * Confidence set estimation From the Inside Flap 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. It is an in-depth introduction to the estimation theory for graduate students, practitioners, and researchers in various fields, such as statistics, engineering, social sciences, and medical sciences. Coverage of the material is designed as a first step in improving the estimates before applying full Bayesian methodology, while problems at the end of each chapter enlarge the scope of the applications.This book contains clear and detailed coverage of basic terminology related to various topics, including:Simple linear model; ANOVA; parallelism model; multiple regression model with non-stochastic and stochastic constraints; regression with autocorrelated errors; ridge regression; and multivariate and discrete data modelsNormal, non-normal, and nonparametric theory of estimationBayes and empirical Bayes methodsR-estimation and U-statisticsConfidence set estimation From the Back Cover 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. It is an in-depth introduction to the estimation theory for graduate students, practitioners, and researchers in various fields, such as statistics, engineering, social sciences, and medical sciences. Coverage of the material is designed as a first step in improving the estimates before applying full Bayesian methodology, while problems at the end of each chapter enlarge the scope of the applications.This book contains clear and detailed coverage of basic terminology related to various topics, including:Simple linear model; ANOVA; parallelism model; multiple regression model with non-stochastic and stochastic constraints; regression with autocorrelated errors; ridge regression; and multivariate and discrete data modelsNormal, non-normal, and nonparametric theory of estimationBayes and empirical Bayes methodsR-estimation and U-statisticsConfidence set estimation About the Author Dr. A. K. Md. Ehsanes Saleh has a very distinguished career in education and research. He is presently Distinguished Research Professor in the School of Mathematics and Statistics at Carleton University. He received his Ph.D. (Mathematics/Statisti