Categorical distance correlation: Concepts, properties and applications
Qingyang Zhang, Associate Professor of Mathematical Sciences, University of Arkansas
Abstract: In this talk, I will introduce categorical distance correlation (CDC), a simple yet powerful statistical functional for assessing dependence in categorical data. After highlighting its empirical power advantages over the traditional Chi-squared test, I will present key theoretical properties of CDC. These include its B-robustness for fixed or diverging numbers of categories, its asymptotic distributions under both null and alternative hypotheses, and the sure screening properties of the maximum likelihood and unbiased estimators. I will also demonstrate its practical utility through an application to General Social Survey data. Time permitting, I will discuss two extensions, including CDC under general encodings (such as one-hot encoding for nominal variables and semi-circle encoding for ordinal variables) and a privacy-preserving framework for CDC.
Cost: free
Kisa Kowal
(847) 491-3974
Email