Doubly robust multiple imputation using kernel-based techniques.

ABSTRACT: 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, p

SUBMITTER: Hsu CH 

PROVIDER: S-EPMC5167998 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature