Document Type

Article

Language

eng

Format of Original

15 p.

Publication Date

6-2014

Publisher

Elsevier

Source Publication

Journal of Statistical Planning and Inference

Source ISSN

0378-3758

Original Item ID

doi: 10.1016/j.jspi.2014.02.001

Abstract

We consider regularization of the parameters in multivariate linear regression models with the errors having a multivariate skew-t distribution. An iterative penalized likelihood procedure is proposed for constructing sparse estimators of both the regression coefficient and inverse scale matrices simultaneously. The sparsity is introduced through penalizing the negative log-likelihood by adding L1-penalties on the entries of the two matrices. Taking advantage of the hierarchical representation of skew-t distributions, and using the expectation conditional maximization (ECM) algorithm, we reduce the problem to penalized normal likelihood and develop a procedure to minimize the ensuing objective function. Using a simulation study the performance of the method is assessed, and the methodology is illustrated using a real data set with a 24-dimensional response vector.

Comments

Accepted version. Journal of Statistical Planning and Inference, Vol. 149 (June 2014): 125-129. DOI. © Elsevier 2014. Used with permission.

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