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Article Dans Une Revue Journal of Multivariate Analysis Année : 2013

Support vector machine quantile regression approach for functional data: Simulation and application studies

Résumé

AMS 2000 subject classifications: 62G08 62G20 62M20 68Q32 62H12 Keywords: Conditional quantile regression Functional covariate Iterative reweighted least squares Reproducing kernel Hilbert space Support vector machine a b s t r a c t The topic of this paper is related to quantile regression when the covariate is a function. The estimator we are interested in, based on the Support Vector Machine method, was introduced in Crambes et al. (2011) [11]. We improve the results obtained in this former paper, giving a rate of convergence in probability of the estimator. In addition, we give a practical method to construct the estimator, solution of a penalized L 1-type minimization problem, using an Iterative Reweighted Least Squares procedure. We evaluate the performance of the estimator in practice through simulations and a real data set study.

Dates et versions

hal-01819413 , version 1 (04-07-2018)

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Christophe Crambes, Ali Gannoun, Yousri Henchiri. Support vector machine quantile regression approach for functional data: Simulation and application studies. Journal of Multivariate Analysis, 2013, 121, pp.50 - 68. ⟨10.1016/j.jmva.2013.06.004⟩. ⟨hal-01819413⟩
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