Title
Optimal Nonparametric Inference on Network Effects with Dependent Edges.
Abstract
Testing network effects in weighted directed networks is a foundational problem in econometrics and sociology. Yet, the prevalent edge dependency poses a significant methodological challenge. Most existing methods are model-based and come with stringent assumptions, limiting their applicability. In response, we introduce a novel, fully nonparametric framework that requires only minimal regularity assumptions. While inspired by recent developments in U-statistic literature, our approach notably broadens their scopes. Specifically, we identified and carefully addressed the challenge of indeterminate degeneracy in the test statistics, a problem that aforementioned tools do not handle. We established Berry-Esseen type bounds for the accuracy of type-I error rate control. With original analysis, we also proved the minimax power optimality of our test. Simulations underscore the superiority of our method in computation speed, accuracy, and numerical robustness compared to competing methods.
Bio
I am an Associate Professor in the Department of Statistics at the Colorado State University and the Department of Biostatistics and Informatics at the Colorado School of Public Health. Before joining CSU, I received my Ph.D. in Statistics at Iowa State University. My research and work are generously supported by NIH, NSF, and DOE. My research is on high dimensional inference, machine learning, modeling and inference on network data, and causal inference, with applications to genomics, proteomics, bioinformatics, social science, econometrics, and political science. I am currently serving as the Co-Editor in Chief for Journal of Biopharmaceutical Statistics, as well as an associate editor for Biometrics , Statistica Sinica , Journal of Multivariate Analysis. I am also serving as the elected WNAR program coordinator since Jan 2024.
