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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
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Previews available in: English
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Edition | Availability |
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1
Theory of Preliminary Test and Stein-Type Estimation with Applications
2006, John Wiley & Sons, Ltd.
Electronic resource
in English
0471773743 9780471773740
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2
Theory of Preliminary Test and Stein-Type Estimation with Applications (Wiley Series in Probability and Statistics)
March 27, 2006, Wiley-Interscience
in English
0471563757 9780471563754
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"In problems of statistical inference, the use of prior information on some or all of the parameters in a statistical model usually leads to improved inference procedures."
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