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10.1093/biomet/ass007 http://scihub22266oqcxt.onion/10.1093/biomet/ass007 C4059083!4059083 !24944347     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=24944347 &cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\24944347 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Biometrika 2012 ; 99 (2 ): 423-438 Nephropedia Template TP {{tp|p=24944347 |t=2012. Multiple imputation in quantile regression |pdf=|usr=}}{{24944347 }} gab.com Text Multiple imputation in quantile regression JO: Biometrika http://www.kidney.de/pget.php?&tw=1&pmid=24944347 #FOAvip We propose a multiple imputation estimator for parameter estimation in a quantile regression model when some covariates are missing at random. The estimation procedure fully utilizes the entire dataset to achieve increased efficiency, and the resulting coefficient estimators are root-n consistent and asymptotically normal. To protect against possible model misspecification, we further propose a shrinkage estimator, which automatically adjusts for possible bias. The finite sample performance of our estimator is investigated in a simulation study. Finally, we apply our methodology to part of the Eating at American's Table Study data, investigating the association between two measures of dietary intake. Twit Text FOAVip Multiple imputation in quantile regression ????Biometrika http://www.kidney.de/pget.php?&tw=1&pmid=24944347 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=24944347 #FOAvip English Wikipedia <ref>{{Cite pmid|24944347 }}</ref> Multiple imputation in quantile regression #MMPMID24944347 Wei Y ; Ma Y ; Carroll RJ Biometrika 2012[]; 99 (2 ): 423-438 PMID24944347 show ga We propose a multiple imputation estimator for parameter estimation in a quantile regression model when some covariates are missing at random. The estimation procedure fully utilizes the entire dataset to achieve increased efficiency, and the resulting coefficient estimators are root-n consistent and asymptotically normal. To protect against possible model misspecification, we further propose a shrinkage estimator, which automatically adjusts for possible bias. The finite sample performance of our estimator is investigated in a simulation study. Finally, we apply our methodology to part of the Eating at American's Table Study data, investigating the association between two measures of dietary intake. äMultiple imputation in quantile regression (epmc).pdf DeepDyve Pubget Overpricing