【主题】Joint Skeleton Estimation of Multiple Directed Acyclic Graphs for Heterogeneous Population

【报告人】Yufeng Liu , 教授

University of North Carolina

【时间】 2019年6月26日（星期三）10:00-11:00

【地点】上海财经大学统计与管理学院大楼1208会议室

【摘要】The directed acyclic graph (DAG) is a powerful tool to model the interactions of high-dimensional variables. While estimating edge directions in a DAG often requires interventional data, one can estimate the skeleton of a DAG (i.e., an undirected graph formed by removing the direction of each edge in a DAG) using observational data. In real data analyses, the samples of the high-dimensional variables may be collected from a mixture of multiple populations. Each population has its own DAG while the DAGs across populations may have signicant overlap. In this talk, we propose a two-step approach to jointly estimate the DAG skeletons of multiple populations while the population origin of each sample may or may not be labeled. In particular, our method allows a probabilistic soft label for each sample, which can be easily computed and often leads to more accurate skeleton estimation than hard labels. Compared with separate estimation of skeletons for each population, our method is more accurate and robust to labeling errors. Theoretical and numerical studies are used to further demonstrate performance of the proposed method.

【嘉宾简介】Dr. Liu is Professor in statistics in Department of Statistics and Operations Research at University of North Carolina, U.S. His research interests include statistical learning techniques for complex and high dimensional data, graphical models, individualized decision rules.

【主持人】冯兴东