Combining Inverse Probability Weighting and Multiple Imputation to Improve Robustness of Estimation
Combining Inverse Probability Weighting and Multiple Imputation to Improve Robustness of Estimation
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DOI：
10.1111/sjos.12177
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发表时间：
2016-03-01
影响因子：
1
通讯作者：
Han, Peisong
中科院分区：
文献类型：
--
作者：
Han, Peisong
逆概率加权法和多重插补法是处理缺失数据的两种常用方法。前者模拟选择概率，后者模拟数据分布。一致的估计需要正确地指定相应的模型。虽然增强的IPW方法提供了一个额外的保护层的一致性，它通常是不够的，在实践中，真正的数据生成过程是未知的。本文提出了一种方法相结合的两种方法在同一精神的校准抽样调查文献。可以同时考虑选择概率和数据分布的多个模型，并且如果正确指定任何模型，则所得估计量是一致的。所提出的方法是估计方程的框架内，是一般足以涵盖缺失的结果和/或缺失的协变量的回归分析。理论和数值研究的结果提供。
Inverse probability weighting (IPW) and multiple imputation are two widely adopted approaches dealing with missing data. The former models the selection probability, and the latter models data distribution. Consistent estimation requires correct specification of corresponding models. Although the augmented IPW method provides an extra layer of protection on consistency, it is usually not sufficient in practice as the true data-generating process is unknown. This paper proposes a method combining the two approaches in the same spirit of calibration in sampling survey literature. Multiple models for both the selection probability and data distribution can be simultaneously accounted for, and the resulting estimator is consistent if any model is correctly specified. The proposed method is within the framework of estimating equations and is general enough to cover regression analysis with missing outcomes and/or missing covariates. Results on both theoretical and numerical investigation are provided.