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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Vydáno v:	Biometrics Ročník 79; číslo 2; s. 1201 - 1212
Hlavní autoři:	Xin, Huiqin, Zhao, Sihai Dave
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Blackwell Publishing Ltd 01.06.2023
Témata:	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
On-line přístup:	Získat plný text
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