吗啡
发表于 2025-3-23 12:04:26
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
发表于 2025-3-23 14:27:50
SpringerBriefs in Statisticshttp://image.papertrans.cn/s/image/866784.jpg
anticipate
发表于 2025-3-23 21:26:07
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隼鹰
发表于 2025-3-24 00:42:15
978-981-15-1595-8The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020
–FER
发表于 2025-3-24 02:56:58
Shrinkage Estimation for Mean and Covariance Matrices978-981-15-1596-5Series ISSN 2191-544X Series E-ISSN 2191-5458
乐章
发表于 2025-3-24 07:42:45
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
鉴赏家
发表于 2025-3-24 13:28:40
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APEX
发表于 2025-3-24 15:46:35
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
发表于 2025-3-24 20:56:45
,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
发表于 2025-3-25 01:57:13
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.