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10.1080/10618600.2014.938812 http://scihub22266oqcxt.onion/10.1080/10618600.2014.938812 C4706754!4706754!26759522     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Comput+Graph+Stat 2015 ; 24 (3): 627-54 Nephropedia Template TP {{tp|p=26759522|t=2015. Learning interactions via hierarchical group-lasso regularization |pdf=|usr=}}{{26759522}} gab.com Text Learning interactions via hierarchical group-lasso regularization JO: J Comput Graph Stat http://www.kidney.de/pget.php?&tw=1&pmid=26759522 #FOAvip We introduce a method for learning pairwise interactions in a linear regression or logistic regression model in a manner that satisfies strong hierarchy: whenever an interaction is estimated to be nonzero, both its associated main effects are also included in the model. We motivate our approach by modeling pairwise interactions for categorical variables with arbitrary numbers of levels, and then show how we can accommodate continuous variables as well. Our approach allows us to dispense with explicitly applying constraints on the main effects and interactions for identifiability, which results in interpretable interaction models. We compare our method with existing approaches on both simulated and real data, including a genome-wide association study, all using our R package glinternet. Twit Text FOAVip Learning interactions via hierarchical group-lasso regularization ????J Comput Graph Stat http://www.kidney.de/pget.php?&tw=1&pmid=26759522 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26759522 #FOAvip English Wikipedia <ref>{{Cite pmid|26759522}}</ref> Learning interactions via hierarchical group-lasso regularization #MMPMID26759522 Lim M; Hastie T J Comput Graph Stat 2015[]; 24 (3): 627-54 PMID26759522show ga We introduce a method for learning pairwise interactions in a linear regression or logistic regression model in a manner that satisfies strong hierarchy: whenever an interaction is estimated to be nonzero, both its associated main effects are also included in the model. We motivate our approach by modeling pairwise interactions for categorical variables with arbitrary numbers of levels, and then show how we can accommodate continuous variables as well. Our approach allows us to dispense with explicitly applying constraints on the main effects and interactions for identifiability, which results in interpretable interaction models. We compare our method with existing approaches on both simulated and real data, including a genome-wide association study, all using our R package glinternet. äLearning interactions via hierarchical group-lasso regularization (epmc).pdf DeepDyve Pubget Overpricing