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This is the preliminary program for the 2007 Joint Statistical Meetings in Salt Lake City, Utah.

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Activity Number: 398
Type: Invited
Date/Time: Wednesday, August 1, 2007 : 10:30 AM to 12:20 PM
Sponsor: IMS
Abstract - #307811
Title: Some Recent Advances in Computing Regularized Quantile Regression Models
Author(s): Ji Zhu*+
Companies: University of Michigan
Address: 439 West Hall, Ann Arbor, MI, 48109-1107,
Keywords: Degrees of freedom ; Linear programming ; Quadratic programming ; Quantile regression ; Regularization ; RKHS
Abstract:

Classical regression methods have mainly focused on estimating conditional mean functions. However, in recent years, quantile regression has emerged as a comprehensive approach to the statistical analysis of response models. In this talk we consider two regularized quantile regression models, the L1-norm (LASSO) regularized quantile regression and quantile regression in reproducing kernel Hilbert spaces. We propose efficient algorithms that compute entire solution paths of the two models. We also derive simple formulas for the effective dimensions of the two models, which allow convenient selection of the regularization parameters.

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Revised September, 2007