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Generalized additive models for functional data

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Abstract

The aim of this paper is to extend the ideas of generalized additive models for multivariate data (with known or unknown link function) to functional data covariates. The proposed algorithm is a modified version of the local scoring and backfitting algorithms that allows for the nonparametric estimation of the link function. This algorithm would be applied to predict a binary response example.

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Acknowledgements

The authors are grateful to the editor and three anonymous referees for their useful comments. This research has been partially funded by project MTM2008-03010 from Ministerio de Ciencia e Innovación, Spain and by project 10MDS207015PR from Xunta de Galicia, Spain.

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Authors and Affiliations

  1. Dpt. of Statistics and O.R. Faculty of Mathematics, University of Santiago de Compostela, Campus Vida 15782, Santiago de Compostela, Spain

    Manuel Febrero-Bande & Wenceslao González-Manteiga

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  1. Manuel Febrero-Bande
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  2. Wenceslao González-Manteiga
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Correspondence to Manuel Febrero-Bande.

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Febrero-Bande, M., González-Manteiga, W. Generalized additive models for functional data. TEST 22, 278–292 (2013). https://doi.org/10.1007/s11749-012-0308-0

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  • DOI: https://doi.org/10.1007/s11749-012-0308-0

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