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10.1007/s12561-013-9104-y http://scihub22266oqcxt.onion/10.1007/s12561-013-9104-y C4465815!4465815!26085848     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 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Stat+Biosci 2015 ; 7 (1): 90-107 Nephropedia Template TP {{tp|p=26085848|t=2015. Semiparametric Bayes local additive models for longitudinal data |pdf=|usr=}}{{26085848}} gab.com Text Semiparametric Bayes local additive models for longitudinal data JO: Stat Biosci http://www.kidney.de/pget.php?&tw=1&pmid=26085848 #FOAvip In longitudinal data analysis, there is great interest in assessing the impact of predictors on the time-varying trajectory in a response variable. In such settings, an important issue is to account for heterogeneity in the shape of the trajectory among subjects, while allowing the impact of the predictors to vary across subjects. We propose a flexible semiparametric Bayes approach for addressing this issue relying on a local partition process prior, which allows flexible local borrowing of information across subjects. Local hypothesis testing and credible bands are developed for the identification of time windows across which a predictor has a significant impact, while adjusting for multiple comparisons. Posterior computation proceeds via an efficient MCMC algorithm using the exact block Gibbs sampler. The methods are assessed using simulation studies and applied to a yeast cell-cycle gene expression data set. Twit Text FOAVip Semiparametric Bayes local additive models for longitudinal data ????Stat Biosci http://www.kidney.de/pget.php?&tw=1&pmid=26085848 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26085848 #FOAvip English Wikipedia <ref>{{Cite pmid|26085848}}</ref> Semiparametric Bayes local additive models for longitudinal data #MMPMID26085848 Hua Z; Zhu H; Dunson DB Stat Biosci 2015[May]; 7 (1): 90-107 PMID26085848show ga In longitudinal data analysis, there is great interest in assessing the impact of predictors on the time-varying trajectory in a response variable. In such settings, an important issue is to account for heterogeneity in the shape of the trajectory among subjects, while allowing the impact of the predictors to vary across subjects. We propose a flexible semiparametric Bayes approach for addressing this issue relying on a local partition process prior, which allows flexible local borrowing of information across subjects. Local hypothesis testing and credible bands are developed for the identification of time windows across which a predictor has a significant impact, while adjusting for multiple comparisons. Posterior computation proceeds via an efficient MCMC algorithm using the exact block Gibbs sampler. The methods are assessed using simulation studies and applied to a yeast cell-cycle gene expression data set. äSemiparametric Bayes local additive models for longitudinal data (epmc).pdf DeepDyve Pubget Overpricing