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Modelling functional additive quantile regression using support vector machines approach

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  • Christophe Crambes
  • Ali Gannoun
  • Yousri Henchiri

Abstract

This work deals with conditional quantiles estimation when several functional covariates are involved, via a support vector machines nonparametric methodology. We establish weak consistency of this estimator. To fit the additive components, we use an ordinary backfitting procedure combined with an iterative reweighted least-squares procedure to solve the penalised minimisation problem. This procedure makes it possible to derive a split sample method for choosing the hyper-parameters of the model. The performances of the proposed technique, in terms of forecast accuracy, are evaluated through simulation and a real dataset study.

Suggested Citation

  • Christophe Crambes & Ali Gannoun & Yousri Henchiri, 2014. "Modelling functional additive quantile regression using support vector machines approach," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 26(4), pages 639-668, December.
    • Handle: RePEc:taf:gnstxx:v:26:y:2014:i:4:p:639-668
    DOI: 10.1080/10485252.2014.941365
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    References listed on IDEAS

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