Description
Book SynopsisTheory 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
Trade 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)
Table of ContentsList 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.
