Regularized multivariate regression models with skew-t error distributions Academic Article uri icon

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. 2014 Elsevier B.V.

published proceedings

  • JOURNAL OF STATISTICAL PLANNING AND INFERENCE

author list (cited authors)

  • Chen, L., Pourahmadi, M., & Maadooliat, M.

citation count

  • 16

complete list of authors

  • Chen, Lianfu||Pourahmadi, Mohsen||Maadooliat, Mehdi

publication date

  • January 1, 2014 11:11 AM