A1276
Title: Dimension reduction for the conditional quantiles of functional data with categorical predictors
Authors: Shanshan Wang - The University of North Carolina at Charlotte (United States) [presenting]
Eliana Christou - University of North Carolina at Charlotte (United States)
Eftychia Solea - Queen Mary University of London (United Kingdom)
Jun Song - Korea University (Korea, South)
Abstract: Functional data analysis has received significant attention due to its frequent occurrence in modern applications, such as in the medical field, where electrocardiograms or electroencephalograms can be used for a better understanding of various medical conditions. Due to the infinite-dimensional nature of functional elements, current work focuses on dimension reduction techniques. The focus is to model the conditional quantiles of functional data, noting that existing works are limited to quantitative predictors. Consequently, the first approach is introduced to partial dimension reduction for the conditional quantiles under the presence of both functional and categorical predictors. The proposed algorithm is presented, and the convergence rates of the estimators are derived. Moreover, the finite sample performance of the method is demonstrated using simulation examples and a real data set based on functional magnetic resonance imaging.
