A0379
Title: On metric choice in dimension reduction for Frechet regression
Authors: Abdul-Nasah Soale - Case Western Reserve University (United States) [presenting]
Abstract: Frechet regression is becoming a mainstay in modern data analysis for analyzing non-traditional data types belonging to general metric spaces. This novel regression method is especially useful in the analysis of complex health data, such as continuous monitoring and imaging data. Frechet regression utilizes the pairwise distances between the random objects, which makes the choice of metric crucial in the estimation. Existing dimension reduction methods for Frechet regression are reviewed, and the effect of metric choice on the estimation of the dimension reduction subspace is explored for the regression between random responses and Euclidean predictors. It illustrates how different metrics affect the central and central mean space estimators. Two real applications involving the analysis of brain connectivity networks of subjects with and without Parkinson's disease and an analysis of the distributions of glycaemia based on continuous glucose monitoring data are provided to demonstrate how metric choice can influence findings in real applications.
