Titlebook: Shrinkage Estimation for Mean and Covariance Matrices; Hisayuki Tsukuma,Tatsuya Kubokawa Book 2020 The Author(s), under exclusive license

licence

Instinctive

虚度

A Generalized Stein Identity and Matrix Differential Operators, very powerful mathematical tool and has contributed significantly to the development of shrinkage estimation. This chapter provides a generalized Stein identity in matrix-variate normal distribution model and also some useful results on matrix differential operators for a unified application of the

广大

DUST

Estimation of the Covariance Matrix,s. With a unified treatment for high and low dimensions, some important improving methods of the best scale and the best triangular invariant estimators are discussed by using the residual sum of squares matrix only. Also this chapter provides interesting dominance results by using the information o

adroit

,Decision-Theoretic Approach to Estimation,erminology of decision-theoretic estimation and shrinkage estimators in the multivariate normal mean estimation. Also, Stein’s unbiased estimate of risk is briefly explained as a general method of how to find better estimators. The unbiased risk estimate method is applied to estimation of mean and covariance matrices discussed in this book.

moratorium

Diastole

A Generalized Stein Identity and Matrix Differential Operators,in identity in matrix-variate normal distribution model and also some useful results on matrix differential operators for a unified application of the identity to high- and low-dimensional normal models.

人充满活力

Estimation of the Mean Matrix,nbiased 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.

STYX