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• W2116937655 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." @default.
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• W2116937655 date "2014-06-01" @default.
• W2116937655 modified "2023-10-17" @default.
• W2116937655 title "Regularized multivariate regression models with skew-t error distributions" @default.
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• W2116937655 doi "https://doi.org/10.1016/j.jspi.2014.02.001" @default.
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