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

Über die Autorin bzw. den Autor

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/Statistics) in 1965 from the University of Western Ontario. He has published or presented well-over 200 articles or topics of varying degrees. His primary research interests include nonparametric statistics, order statistics, and robust estimation. His extensive list of credentials include two dozen visiting appointments and honors, an equal number of theses supervisions, more than 40 keynote or invited speaker ships, and three current editorial board memberships.

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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

Aus dem Klappentext

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