Regularized multivariate regression models with skew-t error distributions
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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. 2014 Elsevier B.V.
JOURNAL OF STATISTICAL PLANNING AND INFERENCE
author list (cited authors)
Chen, L., Pourahmadi, M., & Maadooliat, M.
complete list of authors
Chen, Lianfu||Pourahmadi, Mohsen||Maadooliat, Mehdi