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Shrinkage Estimation for Mean and Covariance MatricesEstimation of the Mean Matrix

Shrinkage Estimation for Mean and Covariance Matrices: Estimation of the Mean Matrix [This chapter introduces a unified approach to high- and low-dimensional cases for matricial shrinkage estimation of a normal mean matrix with unknown covariance matrix. A historical background is briefly explained, and matricial shrinkage estimators are motivated from an empirical Bayes method. An unbiased risk estimate is unifiedly developed for a class of estimators corresponding to all possible orderings of sample size and dimensions. Specific examples of matricial shrinkage estimators are provided and also some related topics are discussed.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Shrinkage Estimation for Mean and Covariance MatricesEstimation of the Mean Matrix

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Publisher
Springer Singapore
Copyright
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020
ISBN
978-981-15-1595-8
Pages
45 –74
DOI
10.1007/978-981-15-1596-5_6
Publisher site
See Chapter on Publisher Site

Abstract

[This chapter introduces a unified approach to high- and low-dimensional cases for matricial shrinkage estimation of a normal mean matrix with unknown covariance matrix. A historical background is briefly explained, and matricial shrinkage estimators are motivated from an empirical Bayes method. An unbiased risk estimate is unifiedly developed for a class of estimators corresponding to all possible orderings of sample size and dimensions. Specific examples of matricial shrinkage estimators are provided and also some related topics are discussed.]

Published: Apr 17, 2020

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