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arXiv · 2208.06940

Kernel-based method for joint independence of functional variables

Terence Kevin Manfoumbi Djonguet·Guy Martial Nkiet
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Abstract

This work investigates the problem of testing whether $d$ functional random variables are jointly independent using a modified estimator of the $d$-variable Hilbert Schmidt Indepedence Criterion ($d$HSIC) which generalizes HSIC for the case where $d \geq 2$. We then get asymptotic normality of this estimator both under joint independence hypothesis and under the alternative hypothesis. A simulation study shows good performance of the proposed test on finite sample.

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Terence Kevin Manfoumbi Djonguet, Guy Martial Nkiet. 2022-08-14. Kernel-based method for joint independence of functional variables. https://arxiv.org/abs/2208.06940

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