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10.1002/bimj.201400256 http://scihub22266oqcxt.onion/10.1002/bimj.201400256 C5167998!5167998!26647734     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       Biom+J 2016 ; 58 (3): 588-606 Nephropedia Template TP {{tp|p=26647734|t=2016. Doubly robust multiple imputation using kernel-based techniques |pdf=|usr=}}{{26647734}} gab.com Text Doubly robust multiple imputation using kernel-based techniques JO: Biom J http://www.kidney.de/pget.php?&tw=1&pmid=26647734 #FOAvip We consider the problem of estimating the marginal mean of an incompletely observed variable and develop a multiple imputation approach. Using fully observed predictors, we first establish two working models: one predicts the missing outcome variable, and the other predicts the probability of missingness. The predictive scores from the two models are used to measure the similarity between the incomplete and observed cases. Based on the predictive scores, we construct a set of kernel weights for the observed cases, with higher weights indicating more similarity. Missing data are imputed by sampling from the observed cases with probability proportional to their kernel weights. The proposed approach can produce reasonable estimates for the marginal mean and has a double robustness property, provided that one of the two working models is correctly specified. It also shows some robustness against misspecification of both models. We demonstrate these patterns in a simulation study. In a real data example, we analyze the total helicopter response time from injury in the Arizona emergency medical service data. Twit Text FOAVip Doubly robust multiple imputation using kernel-based techniques ????Biom J http://www.kidney.de/pget.php?&tw=1&pmid=26647734 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26647734 #FOAvip English Wikipedia <ref>{{Cite pmid|26647734}}</ref> Doubly robust multiple imputation using kernel-based techniques #MMPMID26647734 Hsu CH; He Y; Li Y; Long Q; Friese R Biom J 2016[May]; 58 (3): 588-606 PMID26647734show ga We consider the problem of estimating the marginal mean of an incompletely observed variable and develop a multiple imputation approach. Using fully observed predictors, we first establish two working models: one predicts the missing outcome variable, and the other predicts the probability of missingness. The predictive scores from the two models are used to measure the similarity between the incomplete and observed cases. Based on the predictive scores, we construct a set of kernel weights for the observed cases, with higher weights indicating more similarity. Missing data are imputed by sampling from the observed cases with probability proportional to their kernel weights. The proposed approach can produce reasonable estimates for the marginal mean and has a double robustness property, provided that one of the two working models is correctly specified. It also shows some robustness against misspecification of both models. We demonstrate these patterns in a simulation study. In a real data example, we analyze the total helicopter response time from injury in the Arizona emergency medical service data. äDoubly robust multiple imputation using kernel-based techniques (epmc).pdf DeepDyve Pubget Overpricing