SDS Seminar Series - Arun Kuchibhotla, Carnegie Mellon University

The Spring 2025 SDS Seminar Series begins on March 7th from 2:00 p.m. to 3:00 p.m. with Dr. Arun Kuchibhotl (Assistant Professor, Department of Statistics and Data Science, Carnegie Mellon University). This event is in-person in the Avaya Room (POB 2.302).

Title: Adaptive Inference Techniques for Some Irregular Problems

Abstract: Construction of valid confidence sets (or equivalently p-values) is of paramount importance to any sound statistical study. Traditional methods such as Wald, bootstrap, or subsampling fail to yield reliable uniformly valid inference when the functionals or the statistical models are irregular. Although tailored resampling methods have been proposed for specific irregular problems, no unified inference framework exists. In this talk, I will discuss three new frameworks for adaptive, robustly valid inference that collectively cover many of the common statistical problems. This talk is based on joint work with Sivaraman Balakrishnan, Larry Wasserman, Kenta Takatsu, and Woonyoung Chang, available at https://arxiv.org/abs/2105.14577, https://arxiv.org/abs/2501.07772, https://arxiv.org/abs/2411.17087, and https://arxiv.org/abs/2407.12278.