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A general sparse modeling approach for regression problems involving functional data

Conference paper
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Part of the Contributions to Statistics book series (CONTRIB.STAT.)

Abstract

This presentation aims to introduce an approach for dealing with sparse regression models when functional variables are involved in the statistical sample. The idea is not to restrict to any specific variable selection procedure, but rather to present a two-stage methodology allowing to adapt efficiently any multivariate procedure to the functional framework. These ideas can be applied to any kind of functional regression models, including linear, semi-parametric or non-parametric models.

Keywords

Variable Selection Projection Pursuit Functional Data Analysis Variable Selection Procedure Oracle Property 
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

  1. 1.Departamento de MatemáticasUniversidade da CoruñaA CoruñaSpain
  2. 2.Institut de MathématiquesUniversité Paul SabatierToulouseFrance

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