Statistics Seminar - 09/17/26

Abstract:

Canonical correlation analysis (CCA) is a classical tool for quantifying the dependence between two random vectors, X ∈ R^p and Y ∈ R^q. When X and Y are functional observations, functional canonical correlation analysis (FCCA) extends this framework to characterize the linear association between random functions. In this paper, we develop a novel FCCA framework tailored for discriminant analysis with a categorical response variable Y. The proposed dependence measure is constructed by extending the Gini distance correlation to the functional setting, enabling a rigorous quantification of the association between a functional predictor and a categorical outcome. We establish key theoretical properties of the proposed estimator, including its consistency. Extensive simulation studies demonstrate that the proposed method provides accurate and stable estimation of the underlying dependence structure and exhibits strong performance in discriminant analysis tasks. The practical utility of the approach is further illustrated through a real data application, where a K-sample testing problem is considered.

Note:

Contact Prof. JZ at jzhang@math.msstate.edu for additional information.