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Analysis of Variance in Nonparametric Regression Models

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  • Dette, Holger
  • Derbort, Stephan

Abstract

In a nonparametric regression model with a multivariate explanatory variable we consider the problem of testing the hypothesis that specific interactions in a canonical decomposition of the model vanish. A simple consistent test is developed which is based on the difference between the corresponding sum of squares of the ordinary ANOVA for the multi-factor case and a nonparametric variance estimator. This presents an analogue of the classical ANOVA in the nonparametric regression setup. Asymptotic normality of the introduced test statistic is derived under the null hypothesis and under fixed alternatives. The finite sample behaviour of the proposed procedures is illustrated by a small simulation study and a data example.

Suggested Citation

  • Dette, Holger & Derbort, Stephan, 2001. "Analysis of Variance in Nonparametric Regression Models," Journal of Multivariate Analysis, Elsevier, vol. 76(1), pages 110-137, January.
    • Handle: RePEc:eee:jmvana:v:76:y:2001:i:1:p:110-137
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    References listed on IDEAS

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    1. Basu, A. K., 1980. "On the rate of approximation in the central limit theorem for dependent random variables and random vectors," Journal of Multivariate Analysis, Elsevier, vol. 10(4), pages 565-578, December.
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    Cited by:

    1. Miguel Martínez Comesaña & Sandra Martínez Mariño & Pablo Eguía Oller & Enrique Granada Álvarez & Aitor Erkoreka González, 2020. "A Functional Data Analysis for Assessing the Impact of a Retrofitting in the Energy Performance of a Building," Mathematics, MDPI, vol. 8(4), pages 1-20, April.
    2. Cuevas, Antonio & Febrero, Manuel & Fraiman, Ricardo, 2004. "An anova test for functional data," Computational Statistics & Data Analysis, Elsevier, vol. 47(1), pages 111-122, August.
    3. Felix Abramovich & Italia Feis & Theofanis Sapatinas, 2009. "Optimal testing for additivity in multiple nonparametric regression," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 61(3), pages 691-714, September.

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