Inference with partially missing data.

This project studying the inference of mean when the data are missing not at random. Two sets of estimability conditions are proposed and estimators are proposed for estimating the mean of the data under each condition. Theoretical properties of the estimators are studied. A comprehensive simulation experiment is conducted to compare the performance of our proposed estimator to the existing estimators.