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Nonparametric F-tests for nested global and local polynomial models
Journal article   Peer reviewed

Nonparametric F-tests for nested global and local polynomial models

Li-Shan Huang and Haiyan Su
Journal of Statistical Planning and Inference, Vol.139(4), pp.1372-1380
01/04/2009

Abstract

Local polynomial regression Model checking Nonparametric regression Pseudolikelihood test
In this paper, we investigate geometric properties of local polynomial regression and show that the class of global polynomial models is nested within the class of functions generated by fitting local polynomials. The geometric properties are then utilized to construct nonparametric F-tests for testing whether a regression relationship is a polynomial function. The proposed F-tests can be seen as a "calculus" extension of the classical F-tests with analysis of variance interpretations. With the normality assumption, the test statistic is shown to have asymptotic F-distributions under the null hypothesis and fixed alternatives. Simulation results illustrate that the asymptotic null F-distribution approximates well in finite sample cases and the proposed tests enjoy robustness against heteroscedasticity and non-normality as do the classical F-tests. © 2008 Elsevier B.V. All rights reserved.

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