When analyzing data with missing data, a commonly used method is the inverse probability weighting (IPW) method, which reweights estimating equations with propensity scores. The popularity of the IPW method is due to its simplicity. However, it is often being criticized for being inefficient because most of the information from the incomplete observations is not used. Alternatively, the regression method is known to be efficient but is nonrobust to the misspecification of the regression function. In this article, we propose a novel way of optimally combining the propensity score function and the regression model. The resulting estimating equation enjoys the properties of robustness against misspecification of either the propensity score or the regression function, as well as being locally semiparametric efficient. We demonstrate analytically situations where our method leads to a more efficient estimator than some of its competitors. In a simulation study, we show the new method compares favorably with its competitors in finite samples. Supplementary materials for this article are available online.
Inverse probability weighting, Missing data, Regression estimate, Semiparametric efficiency
Journal of Business and Economic Statistics
Taylor & Francis: STM, Behavioural Science and Public Health Titles
Qin, Jing; Leung, Denis H. Y.; and ZHANG, Biao.
Efficient augmented inverse probability weighted estimation in missing data problems. (2017). Journal of Business and Economic Statistics. 35, (1), 86-97. Research Collection School Of Economics.
Available at: https://ink.library.smu.edu.sg/soe_research/1732
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