The aim of this talk is to highlight the usefulness of kernel methods in regression on functional variables. After reminding some asymptotic properties of the kernel estimator of the regression operator, we introduce a general framework to construct various innovative structural tests (no-efiect, linearity, single-index, … ). Various bootstrap procedures are implemented on datasets in order to emphasize the pertinence of such structural testing methods.
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Delsol, L. (2008). Nonparametric Regression on Functional Variable and Structural Tests. In: Functional and Operatorial Statistics. Contributions to Statistics. Physica-Verlag HD. https://doi.org/10.1007/978-3-7908-2062-1_23
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