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Testing for lack of dependence between functional variables

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  • Aghoukeng Jiofack, Jean Gérard
  • Nkiet, Guy Martial

Abstract

We introduce a test for the lack of dependence between two random variables valued into real Hilbert spaces. Here, we consider lack of dependence in the broader sense, that is, non-correlation. The test statistic is similar to the one proposed by Kokoszka et al. (2008) for testing for no effect in the linear functional model. The asymptotic distribution under the null hypothesis of this statistic is obtained as well as a consistency result for the proposed test. Applications to the case of functional variables are indicated and simulations show, in this context, the performance of the proposed method.

Suggested Citation

  • Aghoukeng Jiofack, Jean Gérard & Nkiet, Guy Martial, 2010. "Testing for lack of dependence between functional variables," Statistics & Probability Letters, Elsevier, vol. 80(15-16), pages 1210-1217, August.
    • Handle: RePEc:eee:stapro:v:80:y:2010:i:15-16:p:1210-1217
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    References listed on IDEAS

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    1. Hervé Cardot & Frédéric Ferraty & André Mas & Pascal Sarda, 2003. "Testing Hypotheses in the Functional Linear Model," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 30(1), pages 241-255, March.
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    Cited by:

    1. Manfoumbi Djonguet, Terence Kevin & Nkiet, Guy Martial, 2024. "An independence test for functional variables based on kernel normalized cross-covariance operator," Journal of Multivariate Analysis, Elsevier, vol. 202(C).
    2. Kokoszka, Piotr & Reimherr, Matthew & Wölfing, Nikolas, 2016. "A randomness test for functional panels," Journal of Multivariate Analysis, Elsevier, vol. 151(C), pages 37-53.

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