An equivalence result for moment equations when data are missing at random
作者机构：Univ RennesEnsaiCNRSCREST–UMR9194F–35000 RennesFrance Center for Research in Economics and Statistics(CREST)Ecole Nationale de la Statistique et de l’Analyse de l’Information(Ensai)Campus de Ker-Lannrue Blaise PascalBP 3720335172 Bruz cedexFrance
出 版 物：《Statistical Theory and Related Fields》 (统计理论及其应用（英文）)
年 卷 期：2019年第3卷第2期
页 面：199-207页
学科分类：07[理学] 0701[理学-数学] 070101[理学-基础数学]
主 题：Efficiency bounds imputation inverse probability weighting semiparametric regression restricted estimators
摘 要：We consider general statistical models defined by moment equations when data are missing atrandom. Using the inverse probability weighting, such a model is shown to be equivalent with amodel for the observed variables only, augmented by a moment condition defined by the missing mechanism. Our framework covers a large class of parametric and semiparametric modelswhere we allow for missing responses, missing covariates and any combination of them. Theequivalence result is stated under minimal technical conditions and sheds new light on variousaspects of interest in the missing data literature, as for instance the efficiency bounds and theconstruction of the efficient estimators, the restricted estimators and the imputation.
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