RT Book,_Section
T1 Variable selection in Functional Additive Regression Models
A1 Febrero Bande, Manuel
A1 González Manteiga, Wenceslao
A1 Oviedo de la Fuente, Manuel
K1 Variable Selection
K1 Distance Correlation
K1 Functional Covariates
K1 Meteorological Information
K1 Scalar Covariates
AB This 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
PB Springer
SN 978-3-319-55845-5
SN 978-3-319-55846-2
YR 2017
FD 2017
LK http://hdl.handle.net/10347/18649
UL http://hdl.handle.net/10347/18649
LA eng
NO Febrero-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
NO This 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: https://doi.org/10.1007/978-3-319-55846-2_15
NO The authors acknowledge financial support from Ministerio de Economía y Competitividad grant MTM2013-41383-P
DS Minerva
RD 24 abr 2026