Noncrossing quantile regression curve estimation.

TitleNoncrossing quantile regression curve estimation.
Publication TypeJournal Article
Year of Publication2010
AuthorsBondell, Howard D., Brian J. Reich, and Huixia Wang
Date Published2010 Dec

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.

Alternate JournalBiometrika
Original PublicationNoncrossing quantile regression curve estimation.
PubMed ID22822254
PubMed Central IDPMC3371721
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States