Xiufan Yu is currently an Assistant Professor of Statistics in the Department of Applied and Computational Mathematics and Statistics, and an affiliate faculty member in Lucy Family Institute for Data & Society at the University of Notre Dame. Prior to that, she received her Ph.D. in Statistics from the Pennsylvania State University, and B.Sc. in Statistics from the School of the Gift Young at University of Science and Technology of China. Her research interests focus on high-dimensional statistics, large-scale statistical inference, causal inference, statistical machine learning, and statistical modeling for interdisciplinary applications.
Talk: Power Enhancement in Statistical Inference for Large and Complex Data
Abstract: Statistical inference has been fundamental for data analysis and decision-making. Different tests may vary in performance across different settings, each excelling in distinct high-power regions. Over the past decade, power enhancement techniques have attracted growing attention in both theoretical and applied statistics, aiming to develop robust tests that remain reliably powerful across a broad spectrum of alternative hypotheses.
In this talk, I will present my recent work on power enhancement in high-dimensional heterogeneous mediation analysis, introducing a powerful inferential method to examine the existence of active mediators in high-dimensional linear and generalized mediation models. Existing tests based on the total indirect effect are often underpowered when the mediation effects are non-homogeneous. To address this limitation, we develop enhanced tests that are proven to maintain strong power under various mediation patterns, including homogeneous, heterogeneous and even contrasting mediation settings. We establish rigorous theoretical guarantees on the power enhancement properties of the proposed tests. Their empirical performances are demonstrated via simulation studies and a real-data application investigating the mediating role of healthcare expenditures in the relationship between economic growth and public health outcomes.