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Lavergne, Pascal
, Maistre, Samuel and Patilea, Valentin
(2014)
A Significance Test for Covariates in Nonparametric Regression.
TSE Working Paper, n. 14-502
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Abstract
We consider testing the significance of a subset of covariates in a nonparamet- ric regression. These covariates can be continuous and/or discrete. We propose a new kernel-based test that smoothes only over the covariates appearing under the null hypothesis, so that the curse of dimensionality is mitigated. The test statistic is asymptotically pivotal and the rate of which the test detects local alternatives depends only on the dimension of the covariates under the null hy- pothesis. We show the validity of wild bootstrap for the test. In small samples, our test is competitive compared to existing procedures.
| Item Type: | Monograph (Working Paper) |
|---|---|
| Language: | English |
| Date: | March 2014 |
| Uncontrolled Keywords: | Testing, Bootstrap, Kernel Smoothing, U−statistic |
| JEL Classification: | C14 - Semiparametric and Nonparametric Methods C52 - Model Evaluation and Selection |
| Subjects: | B- ECONOMIE ET FINANCE |
| Divisions: | TSE-R (Toulouse) |
| Site: | UT1 |
| Date Deposited: | 09 Jul 2014 17:45 |
| Last Modified: | 02 Apr 2021 15:48 |
| OAI Identifier: | oai:tse-fr.eu:28290 |
| URI: | https://publications.ut-capitole.fr/id/eprint/15946 |
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