A compound decision approach to covariance matrix estimation

Covariance matrix estimation is a fundamental statistical task in many applications, but the sample covariance matrix is suboptimal when the sample size is comparable to or less than the number of features. Such high‐dimensional settings are common in modern genomics, where covariance matrix estimat...

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Bibliographic Details
Published in:	Biometrics Vol. 79; no. 2; pp. 1201 - 1212
Main Authors:	Xin, Huiqin, Zhao, Sihai Dave
Format:	Journal Article
Language:	English
Published:	United States Blackwell Publishing Ltd 01.06.2023
Subjects:	Analysis of covariance Animals Bayes Theorem Biostatistics compound decision theory Computer Simulation Covariance matrix Data analysis Eigenvalues Estimation Gene Regulatory Networks genes Genomics g‐modeling Mice nonparametric maximum likelihood Nonparametric statistics Research methodology Sample Size separable decision rule sequence analysis shrinkage variance covariance matrix g-modeling separable decision rule nonparametric maximum likelihood compound decision theory
ISSN:	0006-341X, 1541-0420, 1541-0420
Online Access:	Get full text
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