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.

摘要

我们考虑回归设置中的估计问题，其中结果变量受不可忽略的缺失影响，并且可识别性由影子变量方法确保。我们提出了一种通用的估计程序，其中完全绕过了缺失机制的建模。我们表明我们的估计器易于实现，并且我们推导出所提出的估计器的渐近理论。我们还调查了不同情况下的一些替代估计量。进行了全面的模拟研究，以证明该方法的有限样本性能。我们将该估计量应用于儿童的心理健康研究，以说明其有用性。