Breaking the curse of dimensionality in nonparametric testing |
| |
Authors: | Pascal Lavergne Valentin Patilea |
| |
Affiliation: | 1. Department of Economics, Simon Fraser University, 8888 University Drive, Burnaby BC, Canada V5A 1S6;2. CREST-ENSAI, France |
| |
Abstract: | For tests based on nonparametric methods, power crucially depends on the dimension of the conditioning variables, and specifically decreases with this dimension. This is known as the “curse of dimensionality”. We propose a new general approach to nonparametric testing in high dimensional settings and we show how to implement it when testing for a parametric regression. The resulting test behaves against directional local alternatives almost as if the dimension of the regressors was one. It is also almost optimal against classes of one-dimensional alternatives for a suitable choice of the smoothing parameter. The test performs well in small samples compared to several other tests. |
| |
Keywords: | primary C52 secondary C14 |
本文献已被 ScienceDirect 等数据库收录! |