Work

Sparse Multivariate Regression With Covariance Estimation

Year: 2010

Type: article

Abstract: We propose a procedure for constructing a sparse estimator of a multivariate regression coefficient matrix that accounts for correlation of the response variables. This method, which we call multivariate regression with covariance estimation (MRCE), involves penalized likelihood with simultaneous estimation of the regression coefficients and the covariance structure. An efficient optimization algorithm and a fast approximation are developed for computing MRCE. Using simulation studies, we show that the proposed method outperforms relevant competitors when the responses are highly correlated. We also apply the new method to a finance example on predicting asset returns. An R-package containing this dataset and code for computing MRCE and its approximation are available online.

Source: Journal of Computational and Graphical Statistics

DOI: 10.1198/jcgs.2010.09188

Authors: Adam J. Rothman, Elizaveta Levina, Ji Zhu

Institution: University of Michigan

Language: English

Topic: Statistical Methods and Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, National Institute of General Medical Sciences

Award: Sparse Structure Identification from High-Dimensional Epigenomic Data