Model parameters estimation with non-ignorable missing data using influential exponential tilting resampling approach

This paper proposes to extend [1] mean functional estimation method based on the influential exponential tilting resampling approach (ITRA) to address non-ignorable missing data in linear model parameters statistical inference. The ITRA approach assumes that the nonrespondents’ model corresponds to an exponential tilting of the respondents’ model. The tilted model's specified function is the influential function of the function of interest (parameter). The other basis of the proposed approach is to use the importance resampling techniques to draw inferences about some linear model parameters. Simulation studies were conducted to investigate the performance of the proposed methods and their application to real data. Theoretical justifications are provided as well.