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dc.contributor.authorFebrero Bande, Manuel
dc.contributor.authorGonzález Manteiga, Wenceslao
dc.contributor.authorOviedo de la Fuente, Manuel
dc.identifier.citationFebrero-Bande M., González-Manteiga W., de la Fuente M.O. (2017) Variable selection in Functional Additive Regression Models. In: Aneiros G., G. Bongiorno E., Cao R., Vieu P. (eds) Functional Statistics and Related Fields. Contributions to Statistics. Springer, Cham
dc.descriptionThis is a post-peer-review, pre-copyedit version of an chapter published in Functional Statistics and Related Fields. The final authenticated version is available online at:
dc.description.abstractThis paper considers the problem of variable selection when some of the variables have a functional nature and can be mixed with other type of variables (scalar, multivariate, directional, etc). Our proposal begins with a simple null model and sequentially selects a new variable to be incorporated into the model. For the sake of simplicity, this paper only uses additive models. However, the proposed algorithm may assess the type of contribution (linear, non linear, …) of each variable. The algorithm have showed quite promising results when applied to real data sets
dc.description.sponsorshipThe authors acknowledge financial support from Ministerio de Economía y Competitividad grant MTM2013-41383-P
dc.rights© Springer International Publishing AG 2017
dc.subjectVariable Selection
dc.subjectDistance Correlation
dc.subjectFunctional Covariates
dc.subjectMeteorological Information
dc.subjectScalar Covariates
dc.titleVariable selection in Functional Additive Regression Models
dc.contributor.affiliationUniversidade de Santiago de Compostela. Departamento de Estatística, Análise Matemática e Optimización

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