Handle URI:
http://hdl.handle.net/10754/623594
Title:
Sparse reduced-rank regression with covariance estimation
Authors:
Chen, Lisha; Huang, Jianhua Z.
Abstract:
Improving the predicting performance of the multiple response regression compared with separate linear regressions is a challenging question. On the one hand, it is desirable to seek model parsimony when facing a large number of parameters. On the other hand, for certain applications it is necessary to take into account the general covariance structure for the errors of the regression model. We assume a reduced-rank regression model and work with the likelihood function with general error covariance to achieve both objectives. In addition we propose to select relevant variables for reduced-rank regression by using a sparsity-inducing penalty, and to estimate the error covariance matrix simultaneously by using a similar penalty on the precision matrix. We develop a numerical algorithm to solve the penalized regression problem. In a simulation study and real data analysis, the new method is compared with two recent methods for multivariate regression and exhibits competitive performance in prediction and variable selection.
Citation:
Chen L, Huang JZ (2014) Sparse reduced-rank regression with covariance estimation. Statistics and Computing 26: 461–470. Available: http://dx.doi.org/10.1007/s11222-014-9517-6.
Publisher:
Springer Nature
Journal:
Statistics and Computing
KAUST Grant Number:
KUS-CI-016-04; GRP-CF-2011-19-P-Gao-Huang
Issue Date:
8-Dec-2014
DOI:
10.1007/s11222-014-9517-6
Type:
Article
ISSN:
0960-3174; 1573-1375
Sponsors:
Huang’s work was partially supported by NSF grant DMS-1208952 and by Award Numbers KUS-CI-016-04 and GRP-CF-2011-19-P-Gao-Huang, made by King Abdullah University of Science and Technology (KAUST).
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Full metadata record

DC FieldValue Language
dc.contributor.authorChen, Lishaen
dc.contributor.authorHuang, Jianhua Z.en
dc.date.accessioned2017-05-15T10:35:10Z-
dc.date.available2017-05-15T10:35:10Z-
dc.date.issued2014-12-08en
dc.identifier.citationChen L, Huang JZ (2014) Sparse reduced-rank regression with covariance estimation. Statistics and Computing 26: 461–470. Available: http://dx.doi.org/10.1007/s11222-014-9517-6.en
dc.identifier.issn0960-3174en
dc.identifier.issn1573-1375en
dc.identifier.doi10.1007/s11222-014-9517-6en
dc.identifier.urihttp://hdl.handle.net/10754/623594-
dc.description.abstractImproving the predicting performance of the multiple response regression compared with separate linear regressions is a challenging question. On the one hand, it is desirable to seek model parsimony when facing a large number of parameters. On the other hand, for certain applications it is necessary to take into account the general covariance structure for the errors of the regression model. We assume a reduced-rank regression model and work with the likelihood function with general error covariance to achieve both objectives. In addition we propose to select relevant variables for reduced-rank regression by using a sparsity-inducing penalty, and to estimate the error covariance matrix simultaneously by using a similar penalty on the precision matrix. We develop a numerical algorithm to solve the penalized regression problem. In a simulation study and real data analysis, the new method is compared with two recent methods for multivariate regression and exhibits competitive performance in prediction and variable selection.en
dc.description.sponsorshipHuang’s work was partially supported by NSF grant DMS-1208952 and by Award Numbers KUS-CI-016-04 and GRP-CF-2011-19-P-Gao-Huang, made by King Abdullah University of Science and Technology (KAUST).en
dc.publisherSpringer Natureen
dc.subjectCovariance estimationen
dc.subjectGroup lassoen
dc.subjectReduced-rank regressionen
dc.subjectVariable selectionen
dc.titleSparse reduced-rank regression with covariance estimationen
dc.typeArticleen
dc.identifier.journalStatistics and Computingen
dc.contributor.institutionQuantitative Methodologies, GE Captital, Norwalk, USAen
dc.contributor.institutionDepartment of Statistics, Texas A&M University, College Station, USAen
dc.contributor.institutionThe School of Statistics, Renmin University of China, Beijing, Chinaen
kaust.grant.numberKUS-CI-016-04en
kaust.grant.numberGRP-CF-2011-19-P-Gao-Huangen
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