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10.1093/biomet/asu049 http://scihub22266oqcxt.onion/10.1093/biomet/asu049 C4912060!4912060 !27330194     free     free     free Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\27330194 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Biometrika 2015 ; 102 (1 ): 33-45 Nephropedia Template TP {{tp|p=27330194 |t=2015. Covariance-enhanced discriminant analysis |pdf=|usr=}}{{27330194 }} gab.com Text Covariance-enhanced discriminant analysis JO: Biometrika http://www.kidney.de/pget.php?&tw=1&pmid=27330194 #FOAvip Linear discriminant analysis has been widely used to characterize or separate multiple classes via linear combinations of features. However, the high dimensionality of features from modern biological experiments defies traditional discriminant analysis techniques. Possible interfeature correlations present additional challenges and are often underused in modelling. In this paper, by incorporating possible interfeature correlations, we propose a covariance-enhanced discriminant analysis method that simultaneously and consistently selects informative features and identifies the corresponding discriminable classes. Under mild regularity conditions, we show that the method can achieve consistent parameter estimation and model selection, and can attain an asymptotically optimal misclassification rate. Extensive simulations have verified the utility of the method, which we apply to a renal transplantation trial. Twit Text FOAVip Covariance-enhanced discriminant analysis ????Biometrika http://www.kidney.de/pget.php?&tw=1&pmid=27330194 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27330194 #FOAvip English Wikipedia <ref>{{Cite pmid|27330194 }}</ref> Covariance-enhanced discriminant analysis #MMPMID27330194 Xu P ; Zhu JI ; Zhu L ; Li YI Biometrika 2015[]; 102 (1 ): 33-45 PMID27330194 show ga Linear discriminant analysis has been widely used to characterize or separate multiple classes via linear combinations of features. However, the high dimensionality of features from modern biological experiments defies traditional discriminant analysis techniques. Possible interfeature correlations present additional challenges and are often underused in modelling. In this paper, by incorporating possible interfeature correlations, we propose a covariance-enhanced discriminant analysis method that simultaneously and consistently selects informative features and identifies the corresponding discriminable classes. Under mild regularity conditions, we show that the method can achieve consistent parameter estimation and model selection, and can attain an asymptotically optimal misclassification rate. Extensive simulations have verified the utility of the method, which we apply to a renal transplantation trial. äCovariance-enhanced discriminant analysis (epmc).pdf DeepDyve Pubget Overpricing