Sufficient Dimension Reduction for Conditional Quantiles for Functional Data
Conference
65th ISI World Statistics Congress 2025
Format: IPS Abstract - WSC 2025
Keywords: dimension-reduction, functional data analysis, quantile regression
Session: IPS 914 - Recent Advances on High-Dimensional Statistics for Complex Data
Tuesday 7 October 10:50 a.m. - 12:30 p.m. (Europe/Amsterdam)
Abstract
Functional data analysis is an important research area with the potential to transform numerous fields. However, existing work predominantly relies on the more traditional mean regression methods, with surprisingly limited research focusing on quantile regression. Furthermore, the infinite dimensional nature of the functional predictors necessitates the use of dimension reduction techniques. Therefore, in this work, we address this gap by developing dimension reduction techniques for the conditional quantiles of functional data and apply them to a dataset based on an fMRI study.