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Tests for independence in non-parametric heteroscedastic regression models

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  • Hlávka, Zdenek
  • Husková, Marie
  • Meintanis, Simos G.
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    Abstract

    Consistent procedures are constructed for testing independence between the regressor and the error in non-parametric regression models. The tests are based on the Fourier formulation of independence, and utilize the joint and the marginal empirical characteristic functions of the regressor and of estimated residuals. The asymptotic null distribution as well as the behavior of the test statistic under alternatives is investigated. A simulation study compares bootstrap versions of the proposed tests to corresponding procedures utilizing the empirical distribution function.

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    Bibliographic Info

    Article provided by Elsevier in its journal Journal of Multivariate Analysis.

    Volume (Year): 102 (2011)
    Issue (Month): 4 (April)
    Pages: 816-827

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    Handle: RePEc:eee:jmvana:v:102:y:2011:i:4:p:816-827

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    Related research

    Keywords: Test of independence Empirical characteristic function Kernel regression estimator Bootstrap;

    References

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    Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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    1. Einmahl, J.H.J. & Keilegom, I. van, 2008. "Tests for independence in nonparametric regression," Open Access publications from Tilburg University urn:nbn:nl:ui:12-192434, Tilburg University.
    2. Neumeyer, Natalie, 2009. "Testing independence in nonparametric regression," Journal of Multivariate Analysis, Elsevier, vol. 100(7), pages 1551-1566, August.
    3. Einmahl, J.H.J. & Keilegom, I. van, 2008. "Specification tests in nonparametric regression," Open Access publications from Tilburg University urn:nbn:nl:ui:12-192435, Tilburg University.
    4. Bilodeau, M. & Lafaye de Micheaux, P., 2005. "A multivariate empirical characteristic function test of independence with normal marginals," Journal of Multivariate Analysis, Elsevier, vol. 95(2), pages 345-369, August.
    5. Meintanis, Simos G. & Iliopoulos, George, 2008. "Fourier methods for testing multivariate independence," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 1884-1895, January.
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