Lassoing eigenvalues

David E Tyler and Mengxi Yi

Biometrika, 2020, vol. 107, issue 2, 397-414

Abstract: Summary The properties of penalized sample covariance matrices depend on the choice of the penalty function. In this paper, we introduce a class of nonsmooth penalty functions for the sample covariance matrix and demonstrate how their use results in a grouping of the estimated eigenvalues. We refer to the proposed method as lassoing eigenvalues, or the elasso.

Keywords: Cross-validation; Geodesic convexity; Marchenko–Pastur distribution; Penalization; Principal component; Spiked covariance matrix (search for similar items in EconPapers)
Date: 2020
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