Nonparametric F-tests for nested global and local polynomial models

Li Shan Huang, Haiyan Su

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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.

Original languageEnglish
Pages (from-to)1372-1380
Number of pages9
JournalJournal of Statistical Planning and Inference
Issue number4
StatePublished - 1 Apr 2009


  • Local polynomial regression
  • Model checking
  • Nonparametric regression
  • Pseudolikelihood test


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