Doubly Robust Inference for the Distribution Function in the Presence of Missing Survey Data

Abstract

Item non-response in surveys occurs when some, but not all, variables are missing. Unadjusted estimators tend to exhibit some bias, called the non-response bias, if the respondents differ from the non-respondents with respect to the study variables. In this paper, we focus on item non-response, which is usually treated by some form of single imputation. We examine the properties of doubly robust imputation procedures, which are those that lead to an estimator that remains consistent if either the outcome variable or the non-response mechanism is adequately modelled. We establish the double robustness property of the imputed estimator of the finite population distribution function under random hot-deck imputation within classes. We also discuss the links between our approach and that of Chambers and Dunstan. The results of a simulation study support our findings.

Reference

Hélène Boistard, Guillaume Chauvet, and David Haziza, “Doubly Robust Inference for the Distribution Function in the Presence of Missing Survey Data”, Scandinavian Journal of Statistics, vol. 43, n. 3, September 2016, pp. 683–699.