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10.1016/j.jmva.2017.02.007 http://scihub22266oqcxt.onion/10.1016/j.jmva.2017.02.007 C5628626!5628626!28989203     free     free     free Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\28989203.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       J+Multivar+Anal 2017 ; 157 (ä): 14-28 Nephropedia Template TP {{tp|p=28989203|t=2017. Bayesian sparse reduced rank multivariate regression |pdf=|usr=}}{{28989203}} gab.com Text Bayesian sparse reduced rank multivariate regression JO: J Multivar Anal http://www.kidney.de/pget.php?&tw=1&pmid=28989203 #FOAvip Many modern statistical problems can be cast in the framework of multivariate regression, where the main task is to make statistical inference for a possibly sparse and low-rank coefficient matrix. The low-rank structure in the coefficient matrix is of intrinsic multivariate nature, which, when combined with sparsity, can further lift dimension reduction, conduct variable selection, and facilitate model interpretation. Using a Bayesian approach, we develop a unified sparse and low-rank multivariate regression method to both estimate the coefficient matrix and obtain its credible region for making inference. The newly developed sparse and low-rank prior for the coefficient matrix enables rank reduction, predictor selection and response selection simultaneously. We utilize the marginal likelihood to determine the regularization hyperparameter, so our method maximizes its posterior probability given the data. For theoretical aspect, the posterior consistency is established to discuss an asymptotic behavior of the proposed method. The efficacy of the proposed approach is demonstrated via simulation studies and a real application on yeast cell cycle data. Twit Text FOAVip Bayesian sparse reduced rank multivariate regression ????J Multivar Anal http://www.kidney.de/pget.php?&tw=1&pmid=28989203 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28989203 #FOAvip English Wikipedia <ref>{{Cite pmid|28989203}}</ref> Bayesian sparse reduced rank multivariate regression #MMPMID28989203 Goh G; Dey DK; Chen K J Multivar Anal 2017[May]; 157 (ä): 14-28 PMID28989203show ga Many modern statistical problems can be cast in the framework of multivariate regression, where the main task is to make statistical inference for a possibly sparse and low-rank coefficient matrix. The low-rank structure in the coefficient matrix is of intrinsic multivariate nature, which, when combined with sparsity, can further lift dimension reduction, conduct variable selection, and facilitate model interpretation. Using a Bayesian approach, we develop a unified sparse and low-rank multivariate regression method to both estimate the coefficient matrix and obtain its credible region for making inference. The newly developed sparse and low-rank prior for the coefficient matrix enables rank reduction, predictor selection and response selection simultaneously. We utilize the marginal likelihood to determine the regularization hyperparameter, so our method maximizes its posterior probability given the data. For theoretical aspect, the posterior consistency is established to discuss an asymptotic behavior of the proposed method. The efficacy of the proposed approach is demonstrated via simulation studies and a real application on yeast cell cycle data. äBayesian sparse reduced rank multivariate regression (epmc).pdf DeepDyve Pubget Overpricing