A0349
Title: Central quantile subspace and its extension to functional data
Authors: Eliana Christou - University of North Carolina at Charlotte (United States) [presenting]
Abstract: Quantile regression (QR) is becoming increasingly popular due to its relevance in many scientific investigations. There is a great amount of work on linear and nonlinear QR models. Specifically, the nonparametric estimation of conditional quantiles received particular attention due to its model flexibility. However, nonparametric QR techniques are limited in the number of covariates. Dimension reduction offers a solution to this problem by considering low-dimensional smoothing without specifying any parametric or nonparametric regression relation. The existing dimension reduction techniques focus on the entire conditional distribution. On the other hand, attention is turned to dimension-reduction techniques for conditional quantiles. A new method is introduced for reducing the dimension of predictor X. The methodology's performance is demonstrated through simulation examples and data applications, especially for financial data. Finally, an extension to functional data is presented, including an application on fMRI data.
