Sparse reduced-rank regression with covariance estimation

Lisha Chen, Jianhua Z. Huang

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Improving the predicting performance of the multiple response regression compared with separate linear regressions is a challenging question. On the one hand, it is desirable to seek model parsimony when facing a large number of parameters. On the other hand, for certain applications it is necessary to take into account the general covariance structure for the errors of the regression model. We assume a reduced-rank regression model and work with the likelihood function with general error covariance to achieve both objectives. In addition we propose to select relevant variables for reduced-rank regression by using a sparsity-inducing penalty, and to estimate the error covariance matrix simultaneously by using a similar penalty on the precision matrix. We develop a numerical algorithm to solve the penalized regression problem. In a simulation study and real data analysis, the new method is compared with two recent methods for multivariate regression and exhibits competitive performance in prediction and variable selection.
Original languageEnglish (US)
Pages (from-to)461-470
Number of pages10
JournalStatistics and Computing
Volume26
Issue number1-2
DOIs
StatePublished - Dec 9 2014
Externally publishedYes

Fingerprint Dive into the research topics of 'Sparse reduced-rank regression with covariance estimation'. Together they form a unique fingerprint.

Cite this