Estimation of Large-Dimensional Covariance Matrices via Second-Order Stein-Type Regularization

This paper tackles the problem of estimating the covariance matrix in large-dimension and small-sample-size scenarios. Inspired by the well-known linear shrinkage estimation, we propose a novel second-order Stein-type regularization strategy to generate well-conditioned covariance matrix estimators....

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Detalles Bibliográficos
Autores principales: Zhang, Bin, Huang, Hengzhen, Chen, Jianbin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9857414/
https://www.ncbi.nlm.nih.gov/pubmed/36673194
http://dx.doi.org/10.3390/e25010053

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9857414/
https://www.ncbi.nlm.nih.gov/pubmed/36673194
http://dx.doi.org/10.3390/e25010053

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