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10.1080/10618600.2014.889023 http://scihub22266oqcxt.onion/10.1080/10618600.2014.889023 C4478081!4478081 !26120267     free     free     free Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\26120267 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       J+Comput+Graph+Stat 2015 ; 24 (1 ): 183-204 Nephropedia Template TP {{tp|p=26120267 |t=2015. Graphical Models for Ordinal Data |pdf=|usr=}}{{26120267 }} gab.com Text Graphical Models for Ordinal Data JO: J Comput Graph Stat http://www.kidney.de/pget.php?&tw=1&pmid=26120267 #FOAvip A graphical model for ordinal variables is considered, where it is assumed that the data are generated by discretizing the marginal distributions of a latent multivariate Gaussian distribution. The relationships between these ordinal variables are then described by the underlying Gaussian graphical model and can be inferred by estimating the corresponding concentration matrix. Direct estimation of the model is computationally expensive, but an approximate EM-like algorithm is developed to provide an accurate estimate of the parameters at a fraction of the computational cost. Numerical evidence based on simulation studies shows the strong performance of the algorithm, which is also illustrated on data sets on movie ratings and an educational survey. Twit Text FOAVip Graphical Models for Ordinal Data ????J Comput Graph Stat http://www.kidney.de/pget.php?&tw=1&pmid=26120267 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26120267 #FOAvip English Wikipedia <ref>{{Cite pmid|26120267 }}</ref> Graphical Models for Ordinal Data #MMPMID26120267 Guo J ; Levina E ; Michailidis G ; Zhu J J Comput Graph Stat 2015[Mar]; 24 (1 ): 183-204 PMID26120267 show ga A graphical model for ordinal variables is considered, where it is assumed that the data are generated by discretizing the marginal distributions of a latent multivariate Gaussian distribution. The relationships between these ordinal variables are then described by the underlying Gaussian graphical model and can be inferred by estimating the corresponding concentration matrix. Direct estimation of the model is computationally expensive, but an approximate EM-like algorithm is developed to provide an accurate estimate of the parameters at a fraction of the computational cost. Numerical evidence based on simulation studies shows the strong performance of the algorithm, which is also illustrated on data sets on movie ratings and an educational survey. äGraphical Models for Ordinal Data (epmc).pdf DeepDyve Pubget Overpricing