Shrinkage estimation of large covariance matrices: keep it simple, statistician?

DOI10.1016/j.jmva.2021.104796zbMath1476.62108OpenAlexW3193738839MaRDI QIDQ2237812

Michael Wolf, Olivier Ledoit

Publication date: 28 October 2021

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmva.2021.104796

zbMATH Keywords

random matrix theorylarge-dimensional asymptoticsrotation equivariance

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Ridge regression; shrinkage estimators (Lasso) (62J07) Asymptotic properties of nonparametric inference (62G20) Measures of association (correlation, canonical correlation, etc.) (62H20) Eigenvalues, singular values, and eigenvectors (15A18)

Related Items (2)

Bridging factor and sparse models ⋮ Design-free estimation of integrated covariance matrices for high-frequency data

Uses Software

• QuEST

Cites Work

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