Noncrossing quantile regression curve estimation

Howard D. Bondell, Brian J. Reich and Huixia Wang

Biometrika, 2010, vol. 97, issue 4, 825-838

Abstract: Since quantile regression curves are estimated individually, the quantile curves can cross, leading to an invalid distribution for the response. A simple constrained version of quantile regression is proposed to avoid the crossing problem for both linear and nonparametric quantile curves. A simulation study and a reanalysis of tropical cyclone intensity data shows the usefulness of the procedure. Asymptotic properties of the estimator are equivalent to the typical approach under standard conditions, and the proposed estimator reduces to the classical one if there is no crossing. The performance of the constrained estimator has shown significant improvement by adding smoothing and stability across the quantile levels. Copyright 2010, Oxford University Press.

Date: 2010
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