Kernel-based method for joint independence of functional variables
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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 (dHSIC) which generalizes HSIC for the case where d ≥ 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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