Additive Partially Linear Quantile Regression in Ultra-high Dimension

Ben Sherwood

School of Statistics

University of Minnesota

Engr. Room 1602

4400 University Drive, Fairfax, VA 22030

Time: 12:30 P.M. - 1:30 P.M.

Date: Thursday, Mar 6, 2014 


As high-dimensional data become common in diverse fields, tremendous efforts have recently been devoted to sparse regression problems. Most of the existing work have focused on estimating the conditional mean of the response variable. High-dimensional data can be heterogeneous, for which focusing on the mean function alone may be misleading. Also, it is often assumed that the covariates and response have a linear relationship. To accommodate heterogeneous data and non-linear relationships I will consider a partial linear quantile regression model. The non-convex SCAD penalty is used for simultaneous variable selection and estimation of the linear components. Asymptotic properties of this method are presented along with Monte Carlo simulations and an application to a microarray study using birth weight as a response.

Yunpeng Zhao, PhD

Assistant Professor
Department of Statistics
Volgenau School of Engineering
George Mason University
Engineering Building, Room 1719, MS 4A7
4400 University Drive
Fairfax, VA 22030-4444
Phone: 703-993-1674
Email: [log in to unmask]