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

吗啡

Multivariate Linear Model and Group Invariance,s chapter provides some fundamental properties in terms of the multivariate linear model and the corresponding canonical form. The group invariance is also explained for shrinkage estimation in the multivariate linear model.

易受刺激

SpringerBriefs in Statisticshttp://image.papertrans.cn/s/image/866784.jpg

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隼鹰

978-981-15-1595-8The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020

–FER

Shrinkage Estimation for Mean and Covariance Matrices978-981-15-1596-5Series ISSN 2191-544X Series E-ISSN 2191-5458

乐章

Hisayuki Tsukuma,Tatsuya KubokawaIntegrates modern and classical shrinkage estimation and contributes to further developments in the field.Provides a unified approach to low- and high-dimensional models with respect to the size of th

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APEX

2191-544X - and high-dimensional models with respect to the size of th.This book provides a self-contained introduction to shrinkage estimation for matrix-variate normal distribution models. More specifically, it presents recent techniques and results in estimation of mean and covariance matrices with a high-

synovitis

,Decision-Theoretic Approach to Estimation,ld of decision-theoretic estimation, the most surprising result is the inadmissibility of the sample mean vector in estimation of a mean vector of multivariate normal distribution. The inadmissibility result is closely relevant to the discovery of shrinkage estimator. This chapter summarizes basic t

Habituate

Matrix Algebra,se and some matrix decompositions are required for defining matricial shrinkage estimators. This chapter first explains the notation used in this book and subsequently lists helpful results in matrix algebra.