Graduate Seminar

Location:  RI 209

Speaker: Jin Qin

PhD Student supervised by Yang Zhao

Title: Missing Data: From Basic Methods to Semiparametric Models

 Abstract:

Missing data are a pervasive and often unavoidable challenge in statistical research, potentially leading to biased estimates and reduced statistical efficiency. In this presentation, I will introduce several commonly used methods for handling missing data, including complete case analysis (CC), multiple imputation (MI), inverse probability weighting (IPW), and maximum likelihood (ML). The presentation will then focus on likelihood-based approaches, including fully parametric likelihood, semiparametric likelihood, and a newly proposed pseudo-likelihood method. Finally, I will discuss potential directions for future research aimed at improving the robustness and efficiency of statistical estimation and inference.