Powerful nonparametric checks for quantile regression

Maistre, Samuel, Lavergne, Pascal and Patilea, Valentin (2017) Powerful nonparametric checks for quantile regression. Journal of Statistical Planning and Inference, 180. pp. 13-29.

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Official URL: http://tse-fr.eu/pub/31106


We address the issue of lack-of-fit testing for a parametric quantile regression. We propose a simple test that involves one-dimensional kernel smoothing, so that the rate at which it detects local alternatives is independent of the number of covariates. The test has asymptotically gaussian critical values, and wild bootstrap can be applied to obtain more accurate ones in small samples. Our procedure appears to be competitive with existing ones in simulations. We illustrate the usefulness of our test on birthweight data.

Item Type: Article
Language: English
Date: January 2017
Refereed: Yes
Uncontrolled Keywords: Goodness-of-fit test, U-statistics, Smoothing
JEL codes: C14 - Semiparametric and Nonparametric Methods
C52 - Model Evaluation and Selection
Divisions: TSE-R (Toulouse)
Site: UT1
Date Deposited: 03 Nov 2016 10:58
Last Modified: 07 Mar 2018 13:24
OAI ID: oai:tse-fr.eu:31106
URI: http://publications.ut-capitole.fr/id/eprint/22444

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