Publication: Spectrum estimation: a unified framework for covariance matrix estimation and PCA in large dimensions
Spectrum estimation: a unified framework for covariance matrix estimation and PCA in large dimensions
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Ledoit, O., & Wolf, M. (2013). Spectrum estimation: a unified framework for covariance matrix estimation and PCA in large dimensions (No. 105; Working Paper Series / Department of Economics).
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Covariance matrix estimation and principal component analysis (PCA) are two cornerstones of multivariate analysis. Classic textbook solutions perform poorly when the dimension of the data is of a magnitude similar to the sample size, or even larger. In such settings, there is a common remedy for both statistical problems: nonlinear shrinkage of the eigenvalues of the sample covariance matrix. The optimal nonlinear shrinkage formula depends on unknown population quantities and is thus not available. It is, however, possible to consiste
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Ledoit, O., & Wolf, M. (2013). Spectrum estimation: a unified framework for covariance matrix estimation and PCA in large dimensions (No. 105; Working Paper Series / Department of Economics).
