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Doubly Robust Inference for the Distribution Function in the Presence of Missing Survey Data
Authors:Helene Boistard  Guillaume Chauvet  David Haziza
Institution:1. GREMAQ, Toulouse School of EconomicsUniversité Toulouse 1;2. Laboratoire de Statistique d'EnquêteENSAI;3. Département de mathématiques et de statistiqueUniversité de Montréal
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.
Keywords:distribution function  doubly robust inference  imputation model approach  non‐response model approach
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