A0192
Title: An adaptive approach for sparse quantile regression
Authors: Andreas Artemiou - University of Limassol (Cyprus) [presenting]
Christou Antonis - Cardiff University (United Kingdom)
Abstract: A new approach is presented for the penalization of the quantile regression. We propose an iterative procedure which is based on an approximation of the $L_0$ penalty, and the estimation involves the solution of a quadratic programming optimization problem. We compare our results with the LASSO quantile regression implemented in the quantreg package in R and demonstrate the advantages of our methodology.
