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10.1016/j.spl.2014.06.024 http://scihub22266oqcxt.onion/10.1016/j.spl.2014.06.024 C4111275!4111275!25071299     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Stat+Probab+Lett 2014 ; 93 (ä): 126-33 Nephropedia Template TP {{tp|p=25071299|t=2014. A Modified Adaptive Lasso for Identifying Interactions in the Cox Model with the Heredity Constraint |pdf=|usr=}}{{25071299}} gab.com Text A Modified Adaptive Lasso for Identifying Interactions in the Cox Model with the Heredity Constraint JO: Stat Probab Lett http://www.kidney.de/pget.php?&tw=1&pmid=25071299 #FOAvip In many biomedical studies, identifying effects of covariate interactions on survival is a major goal. Important examples are treatment-subgroup interactions in clinical trials, and gene-gene or gene-environment interactions in genomic studies. A common problem when implementing a variable selection algorithm in such settings is the requirement that the model must satisfy the strong heredity constraint, wherein an interaction may be included in the model only if the interaction?s component variables are included as main effects. We propose a modified Lasso method for the Cox regression model that adaptively selects important single covariates and pairwise interactions while enforcing the strong heredity constraint. The proposed method is based on a modified log partial likelihood including two adaptively weighted penalties, one for main effects and one for interactions. A two-dimensional tuning parameter for the penalties is determined by generalized cross-validation. Asymptotic properties are established, including consistency and rate of convergence, and it is shown that the proposed selection procedure has oracle properties, given proper choice of regularization parameters. Simulations illustrate that the proposed method performs reliably across a range of different scenarios. Twit Text FOAVip A Modified Adaptive Lasso for Identifying Interactions in the Cox Model with the Heredity Constraint ????Stat Probab Lett http://www.kidney.de/pget.php?&tw=1&pmid=25071299 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=25071299 #FOAvip English Wikipedia <ref>{{Cite pmid|25071299}}</ref> A Modified Adaptive Lasso for Identifying Interactions in the Cox Model with the Heredity Constraint #MMPMID25071299 Wang L; Shen J; Thall PF Stat Probab Lett 2014[Oct]; 93 (ä): 126-33 PMID25071299show ga In many biomedical studies, identifying effects of covariate interactions on survival is a major goal. Important examples are treatment-subgroup interactions in clinical trials, and gene-gene or gene-environment interactions in genomic studies. A common problem when implementing a variable selection algorithm in such settings is the requirement that the model must satisfy the strong heredity constraint, wherein an interaction may be included in the model only if the interaction?s component variables are included as main effects. We propose a modified Lasso method for the Cox regression model that adaptively selects important single covariates and pairwise interactions while enforcing the strong heredity constraint. The proposed method is based on a modified log partial likelihood including two adaptively weighted penalties, one for main effects and one for interactions. A two-dimensional tuning parameter for the penalties is determined by generalized cross-validation. Asymptotic properties are established, including consistency and rate of convergence, and it is shown that the proposed selection procedure has oracle properties, given proper choice of regularization parameters. Simulations illustrate that the proposed method performs reliably across a range of different scenarios. äA Modified Adaptive Lasso for Identifying Interactions in the Cox Mode(epmc).pdf DeepDyve Pubget Overpricing