Breaking the Curse of Dimensionality in Nonparametric Testing

Pascal Lavergne and Valentin Patilea
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Valentin Patilea: Crest

No 2006-24, Working Papers from Center for Research in Economics and Statistics

Abstract: For tests based on nonparametric methods, power crucially depends on the dimension of theconditioning variables, and specifically decreases with this dimension. This is known as the"curse of dimensionality." We propose a new general approach to nonparametric testing inhigh dimensional settings and we show how to implement it when testing for a parametricregression. The resulting test behaves against directional local alternatives almost as if thedimension of the regressors was one. It is also almost optimal against classes of onedimensionalalternatives for a suitable choice of the smoothing parameter. A simulationstudy shows that it outperforms the standard test by Zheng (1996).

Pages: 40
Date: 2006
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Citations: View citations in EconPapers (4)

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Related works:
Journal Article: Breaking the curse of dimensionality in nonparametric testing (2008)
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