Nonconvex penalized reduced rank regression and its oracle properties in high dimensions

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

3 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)383-393
Journal / PublicationJournal of Multivariate Analysis
Volume143
Publication statusPublished - 1 Jan 2016
Externally publishedYes

Abstract

Sparse reduced rank regression achieves dimension reduction and variable selection simultaneously. In this paper, for a class of nonconvex penalties, we give sufficient conditions that guarantee the oracle estimator is a local minimizer and stronger conditions that guarantee it is a global minimizer, with probability tending to one in an ultra-high dimensional setting. We carry out simulations to investigate the performance of the estimator. A real data set is analyzed for illustration.

Research Area(s)

  • Model selection, Nonconvex penalty, Oracle property, Reduced rank regression