Transformation and weighting in regression
Transformation and weighting in regression
Simultaneous non-parametric regressions of unbalanced longitudinal data
Computational Statistics & Data Analysis
The covering number in learning theory
Journal of Complexity
On the influence of the kernel on the consistency of support vector machines
The Journal of Machine Learning Research
Nonparametric Functional Data Analysis: Theory and Practice (Springer Series in Statistics)
Nonparametric Functional Data Analysis: Theory and Practice (Springer Series in Statistics)
Editorial: Statistics for Functional Data
Computational Statistics & Data Analysis
An overview to modelling functional data
Computational Statistics
Consistency of kernel-based quantile regression
Applied Stochastic Models in Business and Industry
Representing Functional Data Using Support Vector Machines
CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
Support Vector Machines
Representation of functional data in neural networks
Neurocomputing
Support vector machine for functional data classification
Neurocomputing
Consistency of functional learning methods based on derivatives
Pattern Recognition Letters
Functional classification in Hilbert spaces
IEEE Transactions on Information Theory
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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.