JSM 2004 - Toronto

Abstract #300714

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Activity Number: 116
Type: Contributed
Date/Time: Monday, August 9, 2004 : 10:30 AM to 12:20 PM
Sponsor: General Methodology
Abstract - #300714
Title: Practical Confidence Intervals for Regression Quantiles
Author(s): Yunming Mu*+ and Xuming He and Masha Kocherginsky
Companies: University of Illinois and University of Illinois, Urbana-Champaign and University of Chicago
Address: Department of Statistics, 101 Illini Hall, Champaign, IL, 61820,
Keywords: confidence interval ; Markov chain marginal bootstrap ; regression quantile ; standard error
Abstract:

Routine applications of quantile regression analysis require availability of reliable and practical algorithms for estimating standard errors, variance-covariance matrices as well as confidence intervals. Since the asymptotic variance of a quantile estimator depends on error densities, the standard large sample approximations have been found to be highly sensitive to minor deviations from the i.i.d. error assumption. We propose a time-saving resampling method based on a simple but useful modification of the Markov chain marginal bootstrap to construct confidence intervals in quantile regression. This method is compared to several existing methods with favorable performance in speed, accuracy and reliability. The talk is based on joint work with Masha Kocherginsky and Xuming He.

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Revised March 2004