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10.1093/biomet/asu051 http://scihub22266oqcxt.onion/10.1093/biomet/asu051 C5018402!5018402 !27625437     free     free     free Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\27625437 .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 ): 47-64 Nephropedia Template TP {{tp|p=27625437 |t=2015. Selection and estimation for mixed graphical models |pdf=|usr=}}{{27625437 }} gab.com Text Selection and estimation for mixed graphical models JO: Biometrika http://www.kidney.de/pget.php?&tw=1&pmid=27625437 #FOAvip We consider the problem of estimating the parameters in a pairwise graphical model in which the distribution of each node, conditioned on the others, may have a different exponential family form. We identify restrictions on the parameter space required for the existence of a well-defined joint density, and establish the consistency of the neighbourhood selection approach for graph reconstruction in high dimensions when the true underlying graph is sparse. Motivated by our theoretical results, we investigate the selection of edges between nodes whose conditional distributions take different parametric forms, and show that efficiency can be gained if edge estimates obtained from the regressions of particular nodes are used to reconstruct the graph. These results are illustrated with examples of Gaussian, Bernoulli, Poisson and exponential distributions. Our theoretical findings are corroborated by evidence from simulation studies. Twit Text FOAVip Selection and estimation for mixed graphical models ????Biometrika http://www.kidney.de/pget.php?&tw=1&pmid=27625437 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27625437 #FOAvip English Wikipedia <ref>{{Cite pmid|27625437 }}</ref> Selection and estimation for mixed graphical models #MMPMID27625437 Chen S ; Witten DM ; Shojaie A Biometrika 2015[Mar]; 102 (1 ): 47-64 PMID27625437 show ga We consider the problem of estimating the parameters in a pairwise graphical model in which the distribution of each node, conditioned on the others, may have a different exponential family form. We identify restrictions on the parameter space required for the existence of a well-defined joint density, and establish the consistency of the neighbourhood selection approach for graph reconstruction in high dimensions when the true underlying graph is sparse. Motivated by our theoretical results, we investigate the selection of edges between nodes whose conditional distributions take different parametric forms, and show that efficiency can be gained if edge estimates obtained from the regressions of particular nodes are used to reconstruct the graph. These results are illustrated with examples of Gaussian, Bernoulli, Poisson and exponential distributions. Our theoretical findings are corroborated by evidence from simulation studies. äSelection and estimation for mixed graphical models (epmc).pdf DeepDyve Pubget Overpricing