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Full 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
Contents
List of Figures. List of Tables.
Preface.
1. Introduction.
2. Preliminaries.
3. Preliminary Test Estimation.
4. Stein-Type Estimation.
5. ANOVA Model.
6. Parallelism Model.
7. Multiple Regression Model.
8. Regression Model: Stochastic Subspace.
9. Ridge Regression.
10. Regression Models with Autocorrelated Errors.
11. Multivariate Models.
12. Discrete Data Models.
References.
Glossary.
Authors Index.
Subject Index.
