Semiparametric Inference for Nonmonotone Missing-Not-at-Random Data: The No Self-Censoring Model

• 成果类型: Article
• 署名作者: Malinsky, Daniel; Shpitser, Ilya; Tchetgen, Eric J. Tchetgen
• 署名单位: Columbia University; Johns Hopkins University; University of Pennsylvania
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2020.1862669
• 发表日期: 2022
• 页码: 1415-1423
• 关键词: nonresponse models

摘要

We study the identification and estimation of statistical functionals of multivariate data missing nonmonotonically and not-at-random, taking a semiparametric approach. Specifically, we assume that the missingness mechanism satisfies what has been previously called no self-censoring or itemwise conditionally independent nonresponse, which roughly corresponds to the assumption that no partially observed variable directly determines its own missingness status. We show that this assumption, combined with an odds ratio parameterization of the joint density, enables identification of functionals of interest, and we establish the semiparametric efficiency bound for the nonparametric model satisfying this assumption. We propose a practical augmented inverse probability weighted estimator, and in the setting with a (possibly high-dimensional) always-observed subset of covariates, our proposed estimator enjoys a certain double-robustness property. We explore the performance of our estimator with simulation experiments and on a previously studied dataset of HIV-positive mothers in Botswana. for this article are available online.