Online Publication Date2014-12-09
Print Publication Date2016-01
Permanent link to this recordhttp://hdl.handle.net/10754/623594
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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.
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.
SponsorsHuang’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).
JournalStatistics and Computing