Theory of Preliminary Test and Stein-Type Estimation with Applications
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Theory of Preliminary Test and Stein-Type Estimation with Applications discusses the use of prior information in statistical inference by examining the combined, or merged, roles of preliminary test and empirical Bayes estimations. The author contends that experienced managers, scientists, experimenters, and investigators can gain insight into such information by using the techniques discussed in the book. By so doing, statisticians of varying backgrounds are able to improve the estimators of their parameters before predictions are made.
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
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
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