A Versatile Estimation Procedure Without Estimating the Nonignorable Missingness Mechanism
提出一种在回归分析中处理结果变量不可忽略缺失的通用估计方法，无需建模缺失机制，通过影子变量确保可识别性，并给出渐近理论。
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.