Lassoing eigenvalues

David E. Tyler, Mengxi Yi

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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 languageEnglish (US)
Pages (from-to)397-414
Number of pages18
Issue number2
StatePublished - Jun 1 2020

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Mathematics(all)
  • Agricultural and Biological Sciences (miscellaneous)
  • Agricultural and Biological Sciences(all)
  • Statistics, Probability and Uncertainty
  • Applied Mathematics


  • Cross-validation
  • Geodesic convexity
  • Marchenko-Pastur distribution
  • Penalization
  • Principal component
  • Spiked covariance matrix


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