Doubly robust multiple imputation using kernel-based techniques

Chiu Hsieh Hsu, Yulei He, Yisheng Li, Qi Long, Randall Friese

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

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, 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.

Original languageEnglish (US)
Pages (from-to)588-606
Number of pages19
JournalBiometrical Journal
Volume58
Issue number3
DOIs
StatePublished - May 1 2016

Keywords

  • Bandwidth
  • Bootstrap
  • Local imputation
  • Model misspecification
  • Nonparametric

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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