A Versatile Estimation Procedure Without Estimating the Nonignorable Missingness Mechanism
A Versatile Estimation Procedure Without Estimating the Nonignorable Missingness Mechanism
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DOI：
10.1080/01621459.2021.1893176
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发表时间：
2019-07
影响因子：
3.7
通讯作者：
Jiwei Zhao;Yanyuan Ma
中科院分区：
文献类型：
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作者：
Jiwei Zhao;Yanyuan Ma
摘要我们考虑了回归环境中的估计问题，其中结果变量具有不可忽略的缺失性，并且可识别性由阴影变量方法保证。我们提出了一个通用的估计过程中建模的缺失机制是完全绕过。我们表明，我们的估计是很容易实现的，我们推导出的估计的渐近理论。我们还研究了一些替代估计在不同的情况下。进行了全面的仿真研究，以证明有限样本的方法的性能。我们应用估计儿童的心理健康研究，以说明其有用性。
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.