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Variable Selection in Semi-Functional Regression Models

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Recent Advances in Functional Data Analysis and Related Topics

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

Abstract

We deal with a regression model where a functional covariate enters in a nonparametric way, a divergent number of scalar covariates enter in a linear way and the corresponding vector of regression coefficients is sparse. A penalized-leastsquares based procedure to simultaneously select variables and estimate regression coefficients is proposed, and some asymptotic results are obtained: rates of convengence and oracle property.

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Author information

Authors and Affiliations

  1. Universidade da Coruña, Coruña, Spain

    Germán Aneiros

  2. Institut de Mathématiques de Toulouse, Toulouse, France

    Frédéric Ferraty & Philippe Vieu

Authors
  1. Germán Aneiros
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  2. Frédéric Ferraty
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  3. Philippe Vieu
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Corresponding author

Correspondence to Germán Aneiros .

Editor information

Editors and Affiliations

  1. Toulouse University, Mathematics Toulouse Institute, Narbonne road 118, Toulouse, 31062, France

    Frédéric Ferraty

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Aneiros, G., Ferraty, F., Vieu, P. (2011). Variable Selection in Semi-Functional Regression Models. In: Ferraty, F. (eds) Recent Advances in Functional Data Analysis and Related Topics. Contributions to Statistics. Physica-Verlag HD. https://doi.org/10.1007/978-3-7908-2736-1_3

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