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Abstract Details

Activity Number: 136
Type: Contributed
Date/Time: Monday, August 2, 2010 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Computing
Abstract - #306992
Title: Sparse Multivariate Regression with Covariance Estimation
Author(s): Adam Rothman*+ and Elizaveta Levina and Ji Zhu
Companies: University of Michigan and University of Michigan and University of Michigan
Address: , , ,
Keywords: Sparsity ; multiple output regression ; large p small n
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.


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