A Versatile Estimation Procedure Without Estimating the Nonignorable Missingness Mechanism

2019/07/08 by Jiwei Zhao, Yanyuan Ma, Zhao, Jiwei +1 · 3 citations
Mathematics · #Advanced Causal Inference Techniques #FOS: Computer and information sciences #Methodology (stat.ME) #Statistical Methods and Bayesian Inference #Statistical Methods and Inference

paper · pdf · doi:10.48550/arxiv.1907.03682

openalex publication_date 2019/07/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We consider the estimation problem in a regression setting where the outcome variable is subject to nonignorable missingness and identifiability is ensured by the shadow variable approach. We propose a versatile estimation procedure where modeling of missingness mechanism is completely bypassed. We show that our estimator is easy to implement and we derive the asymptotic theory of the proposed estimator. We also investigate some alternative estimators under different scenarios. Comprehensive simulation studies are conducted to demonstrate the finite sample performance of the method. We apply the estimator to a children's mental health study to illustrate its usefulness.

Citations

• Estimation of Regression Coefficients When Some Regressors are not Always Observed
• Semiparametric Optimal Estimation With Nonignorable Nonresponse Data

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