B1903
Title: Multiple bias calibration for valid statistical inference under non-ignorability
Authors: Yumou Qiu - Peking University (China) [presenting]
Abstract: Valid statistical inference is notoriously challenging when the sample is subject to nonresponse bias. The difficult problem is approached by employing multiple candidate models for the propensity score function combined with empirical likelihood. By incorporating multiple propensity score (PS) models into the internal bias calibration constraint in the empirical likelihood setup, the selection bias can be safely eliminated as long as the multiple candidate models contain the true PS model. The bias calibration constraint for the multiple PS model in the empirical likelihood is called the multiple bias calibration. The multiple PS models can include both ignorable and nonignorable models. It delves into the asymptotic properties of the proposed method and provides a comparative analysis through limited simulation studies against existing methods. To illustrate practical implementation, an application is presented using the national health and nutrition examination survey (NHANES) dataset.
