*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: 1**2**:**3**0 **P**.M. - **1**:**3**0 **P**.M.*

*Date: **Thursday**, **Mar 6**, 201**4*

*Abstract*

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 <[log in to unmask]>
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]