Abstract
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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 397-414 |
| Number of pages | 18 |
| Journal | Biometrika |
| Volume | 107 |
| Issue number | 2 |
| DOIs | |
| State | Published - Jun 1 2020 |
All Science Journal Classification (ASJC) codes
- Statistics and Probability
- General Mathematics
- Agricultural and Biological Sciences (miscellaneous)
- General Agricultural and Biological Sciences
- Statistics, Probability and Uncertainty
- Applied Mathematics
Keywords
- Cross-validation
- Geodesic convexity
- Marchenko-Pastur distribution
- Penalization
- Principal component
- Spiked covariance matrix
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