Regularized multivariate regression models with skew-t error distributions

Handle URI:
http://hdl.handle.net/10754/599488
Title:
Regularized multivariate regression models with skew-t error distributions
Authors:
Chen, Lianfu; Pourahmadi, Mohsen; Maadooliat, Mehdi
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.
Citation:
Chen L, Pourahmadi M, Maadooliat M (2014) Regularized multivariate regression models with skew-t error distributions. Journal of Statistical Planning and Inference 149: 125–139. Available: http://dx.doi.org/10.1016/j.jspi.2014.02.001.
Publisher:
Elsevier BV
Journal:
Journal of Statistical Planning and Inference
KAUST Grant Number:
KUS-CI-016-04
Issue Date:
Jun-2014
DOI:
10.1016/j.jspi.2014.02.001
Type:
Article
ISSN:
0378-3758
Sponsors:
We would like to thank two referees for their constructive comments and suggestions. The second author was supported by the National Science Foundation (Grants DMS-0906252 and DMS-1309586), and the third author was partially supported by King Abdullah University of Science and Technology (Grant KUS-CI-016-04).
Appears in Collections:
Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorChen, Lianfuen
dc.contributor.authorPourahmadi, Mohsenen
dc.contributor.authorMaadooliat, Mehdien
dc.date.accessioned2016-02-28T05:52:03Zen
dc.date.available2016-02-28T05:52:03Zen
dc.date.issued2014-06en
dc.identifier.citationChen L, Pourahmadi M, Maadooliat M (2014) Regularized multivariate regression models with skew-t error distributions. Journal of Statistical Planning and Inference 149: 125–139. Available: http://dx.doi.org/10.1016/j.jspi.2014.02.001.en
dc.identifier.issn0378-3758en
dc.identifier.doi10.1016/j.jspi.2014.02.001en
dc.identifier.urihttp://hdl.handle.net/10754/599488en
dc.description.abstractWe 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.en
dc.description.sponsorshipWe would like to thank two referees for their constructive comments and suggestions. The second author was supported by the National Science Foundation (Grants DMS-0906252 and DMS-1309586), and the third author was partially supported by King Abdullah University of Science and Technology (Grant KUS-CI-016-04).en
dc.publisherElsevier BVen
dc.subjectCross-validationen
dc.subjectECM algorithmen
dc.subjectLasso regressionen
dc.subjectLikelihood functionen
dc.subjectMultivariate skew-ten
dc.subjectPenaltyen
dc.titleRegularized multivariate regression models with skew-t error distributionsen
dc.typeArticleen
dc.identifier.journalJournal of Statistical Planning and Inferenceen
dc.contributor.institutionTexas A and M University, College Station, United Statesen
dc.contributor.institutionMarquette University, Milwaukee, United Statesen
kaust.grant.numberKUS-CI-016-04en
All Items in KAUST are protected by copyright, with all rights reserved, unless otherwise indicated.