Hang Zhou of UNC-Chapel Hill : Statistical Inference for Random Objects

Posted by Yichao Wu , part of the Statistics and Data Science Seminar.

At

Oct. 14, 2026, 4:30 p.m.

In

636 SEO

Abstract

Random objects are complex random variables taking values in general metric spaces. Although such data are increasingly common in scientific research, current statistical methodology and theory remain limited. The primary challenge in analyzing such data lies in the absence of vector space operations, such as addition, subtraction, scalar multiplication, and inner products, which are fundamental tools in conventional statistical methodologies. This talk explores object data with distance profiles and their application to conformal prediction and independence testing.