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Variable selection in Functional Additive Regression Models

  • Manuel Febrero-BandeEmail author
  • Wenceslao González-Manteiga
  • Manuel Oviedo de la Fuente
Conference paper
Part of the Contributions to Statistics book series (CONTRIB.STAT.)

Abstract

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.

Keywords

Variable Selection Distance Correlation Functional Covariates Meteorological Information Scalar Covariates 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Manuel Febrero-Bande
    • 1
    Email author
  • Wenceslao González-Manteiga
    • 1
  • Manuel Oviedo de la Fuente
    • 2
  1. 1.Department of Statistics, Mathematical Analysis and OptimizationUniversidade de Santiago de CompostelaSantiagoSpain
  2. 2.Technological Institute for Industrial Mathematics and Department of Statistics, Mathematical Analysis and OptimizationUniversidade de Santiago de CompostelaSantiagoSpain

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