Theory of Preliminary Test and Stein-Type Estimation with Applications (Wiley Series in Probability and Statistics)
A. K. Md. Ehsanes Saleh | 2006-03-27 00:00:00 | Wiley-Interscience | 656 | Mathematics
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
Reviews
This book has recently been published by Wiley and Sons. The author of the book is one of the distinguished researchers who has contributed to the extension and refinement of the preliminary test and stein-type estimators. The author is also a pioneer of introducing the two methodologies in non-parametric theory.
The book contains a great wealth of information on the classical, restricted, preliminary test, James-Stein, positive-rule, ridge, Bays, empirical Bayes, and non-parametric estimators. A great deal of materials on mixed estimation with stochastic and non-stochastic constraints on the model's parameters have also been provided. The above stated estimators have been constructed for the estimation of unkown parameters in a number of models such as; analysis of variance, parallelism, multiple regression, multivariate regression, regression with autocorrelated errors, and discrete models. Their performances have been thoroughly studied via quadratic biases, mean squares error matrices, and qudratic risk functions both in small and large samples setups. The quadratic biases, risks, and maximum ansd minimum effiencies of the estimators have been tabulated and gaphically portrayed.
This is the first ever book of statistics that provides minute details on the theory and applications of preliminary test and Stein-type estimation in a very simple manner. The book has impressed me so immensely that I have to appreciate the efforts and depth of the author's knowledge about the subject.
No doubt, the book is an asset for the graduate students, researchers, and teachers particularly in the ares of preliminary test and James-Stein estimation and in the discipline of statistics in general .
I would grade the book to be 4.8 out of 5.
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