Tools
Tools
Lavergne, Pascal
, Maistre, Samuel and Patilea, Valentin
(2015)
A Significance Test for Covariates in Nonparametric Regression.
Electronic Journal of Statistics, vol. 9.
pp. 643-678.
This is the latest version of this item.
Preview |
Text
Download (370kB) | Preview |
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: | Article |
|---|---|
| Language: | English |
| Date: | 2015 |
| Refereed: | Yes |
| 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: | 21 Sep 2015 13:08 |
| Last Modified: | 02 Apr 2021 15:49 |
| OAI Identifier: | oai:tse-fr.eu:29256 |
| URI: | https://publications.ut-capitole.fr/id/eprint/16880 |
Available Versions of this Item
-
A Significance Test for Covariates in Nonparametric Regression. (deposited 09 Jul 2014 17:45)
- A Significance Test for Covariates in Nonparametric Regression. (deposited 21 Sep 2015 13:08) [Currently Displayed]
