JSM 2005 - Toronto

Abstract #304435

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 362
Type: Contributed
Date/Time: Wednesday, August 10, 2005 : 8:30 AM to 10:20 AM
Sponsor: Section on Nonparametric Statistics
Abstract - #304435
Title: Empirical Likelihood-based Inference Procedure for Quantile Regression
Author(s): Mi-Ok Kim*+ and Mai Zhou
Companies: University of Kentucky and University of Kentucky
Address: 817 Patterson Office Tower, Lexington, KY, 40506, United States
Keywords: quantile regression ; censored quantile regression ; empirical likelihood
Abstract:

We propose an inference procedure for quantile regression based on empirical likelihood that can accommodate censoring. For uncensored quantile regression, regression rankscore method (Gutenbrunner and Jureckova 1991, Koenker 1994) is known to provide a reliable confidence interval. We compare the proposed procedure with regression rankscore method. The performances are compared via simulation with a small or moderate size sample and by analytic results with a large sample. For censored quantile regression where usual quantile inference procedures do not apply, we compare the proposed method with bootstrap-based method.

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