A note on multiple imputation under complex sampling

J. K. Kim and S. Yang

Biometrika, 2017, vol. 104, issue 1, 221-228

Abstract: SUMMARY Multiple imputation is popular for handling item nonresponse in survey sampling. Current multiple imputation techniques with complex survey data assume that the sampling design is ignorable. In this paper, we propose a new multiple imputation procedure for parametric inference without this assumption. Instead of using the sample-data likelihood, we use the sampling distribution of the pseudo maximum likelihood estimator to derive the posterior distribution of the parameters. The asymptotic properties of the proposed method are investigated. A simulation study confirms that the new procedure provides unbiased point estimation and valid confidence intervals with correct coverage properties whether or not the sampling design is ignorable.

Keywords: Approximate Bayesian computation; Bayesian inference; Informative sampling; Item nonresponse; Pseudo maximum likelihood estimator (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (1)

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