Statistics and Data Science Seminar: Pattern Graphs: a Graphical Approach to Nonmonotone Missing Data

Abstract: We introduce the concept of pattern graphs--directed acyclic graphs representing how response patterns are associated. A pattern graph represents an identifying restriction that is nonparametrically identified/saturated and is often a missing not at random restriction. We introduce a selection model and a pattern mixture model formulation using the pattern graphs and show that they are equivalent. A pattern graph leads to an inverse probability weighting estimator as well as an imputation-based estimator. We also study the semi-parametric efficiency theory and derive a multiply-robust estimator using pattern graphs.

Bio: Dr. Chen is an associate professor in the Department of Statistics and a data science fellow in the eScience Institute at the University of Washington. He also serves as a co-investigator and statistician at the National Alzheimer’s Coordinating Center. Dr. Chen has received several awards including NSF's CAREER award and ASA's Noether Young scholar award.

This is a virtual talk over Zoom. Please join at the following link: https://wustl.zoom.us/j/97474245459 

Host: Robert Lunde