Simultaneous multiple response regression and inverse covariance matrix estimation via penalized Gaussian maximum likelihood

JOURNAL OF MULTIVARIATE ANALYSIS

卷:111

Article

Lee, Wonyul¹  Liu, Yufeng¹ 

[1] Univ N Carolina, Carolina Ctr Genome Sci, Dept Stat & Operat Res, Chapel Hill, NC 27599 USA

关键词: GLASSO;    Inverse covariance matrix estimation;    Joint estimation;    LASSO;    Multiple response;    Sparsity;   

DOI  :  10.1016/j.jmva.2012.03.013

来源: Elsevier

【 摘 要 】

Multivariate regression is a common statistical tool for practical problems. Many multivariate regression techniques are designed for univariate response cases. For problems with multiple response variables available, one common approach is to apply the univariate response regression technique separately on each response variable. Although it is simple and popular, the univariate response approach ignores the joint information among response variables. In this paper, we propose three new methods for utilizing joint information among response variables. All methods are in a penalized likelihood framework with weighted L-1 regularization. The proposed methods provide sparse estimators of the conditional inverse covariance matrix of the response vector, given explanatory variables as well as sparse estimators of regression parameters. Our first approach is to estimate the regression coefficients with plug-in estimated inverse covariance matrices, and our second approach is to estimate the inverse covariance matrix with plug-in estimated regression parameters. Our third approach is to estimate both simultaneously. Asymptotic properties of these methods are explored. Our numerical examples demonstrate that the proposed methods perform competitively in terms of prediction, variable selection, as well as inverse covariance matrix estimation. (C) 2012 Elsevier Inc. All rights reserved.

【 授权许可】

Free