ArticleJournal of the American Statistical Association2022
Semiparametric Inference for Nonmonotone Missing-Not-at-Random Data: The No Self-Censoring Model.
Article in Journal of the American Statistical Association, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Trial
- A Doubly Robust Approach to Evaluating the Impact of Hospital Length of Stay on Post-Fracture Recovery in Older Adults With Cognitive Impairment.Statistics in medicine · 2026Article
- Accelerating real-world prediction and research in Alzheimer's: The M3AD study.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Investigations of sharp bounds for causal effects under selection bias.Statistical methods in medical research · 2025Article
- Robust causal inference for point exposures with missing confounders.The Canadian journal of statistics = Revue canadienne de statistique · 2025Article
- Double Sampling for Informatively Missing Data in Electronic Health Record-Based Comparative Effectiveness Research.Statistics in medicine · 2024Article
- Analysis of Missingness Scenarios for Observational Health Data.Journal of personalized medicine · 2024Article
- Methods in causal inference. Part 3: measurement error and external validity threats.Evolutionary human sciences · 2024Article
- Implementing and validating a home-infusion central-line-associated bloodstream infection surveillance definition.Infection control and hospital epidemiology · 2023Article
- Methods for handling missing binary data in substance use disorder trials.Drug and alcohol dependence · 2023Review
- Article
- Controlling the chaos: Information management in home-infusion central-line-associated bloodstream infection (CLABSI) surveillance.Antimicrobial stewardship & healthcare epidemiology : ASHE · 2023Article
- A task analysis of central line-associated bloodstream infection (CLABSI) surveillance in home infusion therapy.American journal of infection control · 2022Article
- A Robust Functional EM Algorithm for Incomplete Panel Count Data.Advances in neural information processing systems · 2020Article
- Full Law Identification in Graphical Models of Missing Data: Completeness Results.Proceedings of machine learning research · 2020Article
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Authors and funding
3 authors.
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Abstract
We study the identification and estimation of statistical functionals of multivariate data missing non-monotonically 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 data set of HIV-positive mothers in Botswana.
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