Regularized multivariate regression models with skew-t error distributions

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.

authors

• Pourahmadi, Mohsen

published proceedings

• JOURNAL OF STATISTICAL PLANNING AND INFERENCE

altmetric score

• 3

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 2014

publisher

• Elsevier  Publisher

published in

• Journal of Statistical Planning and Inference  Journal

keywords

• Cross-validation
• Ecm Algorithm
• Lasso Regression
• Likelihood Function
• Multivariate Skew-t
• Penalty

Digital Object Identifier (DOI)

• 10.1016/j.jspi.2014.02.001

start page

• 125

end page

• 139

volume

• 149

URL

• http%3A%2F%2Fdx.doi.org%2F10.1016%2Fj.jspi.2014.02.001