B0643
Title: Robustness against model misspecifications in missing data analysis
Authors: Peisong Han - University of Waterloo (Canada) [presenting]
Abstract: Methods that are robust against model misspecifications are highly desired. In missing data analysis, doubly robust methods have received wide attention due to their double protection on estimation consistency. Doubly robust estimators are consistent if either the model for selection probability or the model for data distribution is correctly specified. We propose a method that exhibits a further improved robustness. This method can simultaneously account for multiple models for both selection probability and data distribution. The resulting estimators are consistent if any one model is correctly specified, without knowing exactly which one it is. When both selection probability and data distribution are correctly modeled, the resulting estimators achieve maximum possible efficiency, again without knowing which models are the correct ones. This new method is based on the calibration idea in sampling survey literature, and has a strong connection to empirical likelihood. Another superior property of the multiply robust estimators is that, unlike many existing ones, they are not sensitive to near-zero values of estimated selection probabilities. Simulation evidence will also be presented to demonstrate the excellent numerical performance of the new method.
