Jiwei Zhao
State University of New York at Buffalo

Nonignorable Missingness Mechanism Model Can Be Ignored

Nonignorable missing data exist in various biomedical studies and social sciences, e.g., aging research, metabolomics data analysis, electronic medical records, and health surveys. A major hurdle of rigorous nonignorable missing data analysis is how to model or estimate the missingness mechanism. Since this model depends on some unobserved data, its model fitting and model diagnostics are generally regarded as difficult, if not impossible. In this talk, I will consider a regression setting where the outcome variable is subject to nonignorable missingness. The primary interest is to estimate the unknown parameter in the regression model. I will discuss an estimation procedure where modeling of missingness mechanism is completely bypassed. I will show the asymptotic properties of the proposed estimator and the algorithm implementation. Numerical studies will also be presented to illustrate the usefulness of our proposed estimation. This talk is based on a joint work with Dr. Yanyuan Ma from Penn State University.

Refreshments at 3:45pm in Snedecor 2101.