Online Program Home
  My Program

All Times EDT

Abstract Details

Activity Number: 403 - Sufficient Dimension Reduction and Variable Selection for High-Dimensional Inference
Type: Topic Contributed
Date/Time: Wednesday, August 5, 2020 : 1:00 PM to 2:50 PM
Sponsor: Section on Nonparametric Statistics
Abstract #309656
Title: Principal Asymmetric Least Squares for Sufficient Dimension Reduction
Author(s): Yuexiao Dong* and Abdul-Nasah Soale
Companies: Temple University and Temple University
Keywords: asymmetric least squares; expectile regression; nonlinear dimension reduction

We introduce principal asymmetric least squares (PALS) for linear and nonlinear sufficient dimension reduction. PALS extends the seminal work of principal support vector machines (PSVM) (Artemiou and Li, 2011) and replaces the hinge loss with the asymmetric least squares loss. By synthesizing different expectile levels in the loss function, PALS can handle heteroscedasticity better than PSVM. PALS leads to unbiased estimators at the population level and is attractive computationally. The superior empirical performance of the proposed method is demonstrated through extensive simulation studies and a real data analysis.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2020 program