Multiple Functional Regression with both Discrete and Continuous Covariates

  • Hachem Kadri
  • Philippe Preux
  • Emmanuel Duflos
  • Stéphane Canu
Conference paper
Part of the Contributions to Statistics book series (CONTRIB.STAT.)


In this paper we present a nonparametric method for extending functional regression methodology to the situation where more than one functional covariate is used to predict a functional response. Borrowing the idea from Kadri et al. (2010a), the method, which support mixed discrete and continuous explanatory variables, is based on estimating a function-valued function in reproducing kernel Hilbert spaces by virtue of positive operator-valued kernels.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Hachem Kadri
    • 1
  • Philippe Preux
    • 2
  • Emmanuel Duflos
    • 1
  • Stéphane Canu
    • 3
  1. 1.INRIA Lille - Nord Europe/Ecole Centrale de LilleVilleneuve d’AscqFrance
  2. 2.INRIA Lille - Nord Europe/Université de LilleVilleneuve d’AscqFrance
  3. 3.INSA de RouenSt Etienne du RouvrayFrance

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