Generalized additive models for functional data
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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.
KeywordsFunctional data Generalized additive models Generalized linear models
Mathematics Subject Classification62G08 62J12
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.
- Fan Y, James G (2012) Functional additive regression. Preprint, available at http://www-bcf.usc.edu/~gareth/research/FAR.pdf
- Febrero-Bande M, Oviedo de la Fuente M (2012a) fda.usc: functional data analysis. Utilities for Statistical Computing. URL http://cran.r-project.org/web/packages/fda.usc/index.html. R package version 0.9.8
- Febrero-Bande M, Oviedo de la Fuente M (2012b) Statistical computing in functional data analysis: the R package fda.usc. J Stat Softw, preprint Google Scholar
- Ramsay JO, Silverman BW (2005) Functional data analysis, 2nd edn. Springer, Berlin Google Scholar