Lassoing eigenvalues

• 成果类型: Article
• 署名作者: Tyler, David E.; Yi, Mengxi
• 署名单位: Rutgers University System; Rutgers University New Brunswick; University of International Business & Economics
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asz076
• 发表日期: 2020
• 页码: 397414
• 关键词: Covariance matrices

摘要

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.