Modelling functional additive quantile regression using support vector machines approach

Christophe Crambes, Ali Gannoun and Yousri Henchiri

Journal of Nonparametric Statistics, 2014, vol. 26, issue 4, 639-668

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.

Date: 2014
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DOI: 10.1080/10485252.2014.941365

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