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Bandwidth Selection Problem in Nonparametric Functional Regression

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KURUCZOVÁ Daniela KOLÁČEK Jan

Rok publikování 2017
Druh Článek v odborném periodiku
Časopis / Zdroj Statistika: Statistics and Economy Journal
Fakulta / Pracoviště MU

Přírodovědecká fakulta

Citace
www Statistika: Statistics and Economy Journal
Obor Obecná matematika
Klíčová slova Functional data; nonparametric regression; kernel methods; bandwidth selection
Popis The focus of this paper is the nonparametric regression where the predictor is a functional random variable, and the response is a scalar. Functional kernel regression belongs to popular nonparametric methods used for this purpose. The two key problems in functional kernel regression are choosing an optimal smoothing parameter and selecting an appropriate semimetric as a distance measure. The former is the focus of this paper – several data-driven methods for optimal bandwidth selection are described and discussed. The performance of these methods is illustrated in a real data application. A conclusion is drawn that local bandwidth selection methods are more appropriate in the functional setting.
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