Weak consistency of the support vector machine quantile regression approach when covariates are functions

DOI10.1016/j.spl.2011.07.008zbMath1225.62050OpenAlexW2048677519MaRDI QIDQ645434

Ali Gannoun, Yousri Henchiri, Christophe Crambes

Publication date: 15 November 2011

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.spl.2011.07.008

zbMATH Keywords

reproducing kernel Hilbert spacesupport vector machineconditional quantile regressionfunctional covariatesill-conditioned inverse problem

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Multivariate analysis (62H99) Asymptotic properties of nonparametric inference (62G20) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (3)

Inference in functional linear quantile regression ⋮ Modelling functional additive quantile regression using support vector machines approach ⋮ Non parametric learning approach to estimate conditional quantiles in the dependent functional data case

Uses Software

• fda (R)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Regression models for functional data by reproducing kernel Hilbert spaces methods
• Functional data analysis
• The covering number in learning theory
• Some results on Tchebycheffian spline functions and stochastic processes
• 10.1162/153244302760185252
• Support Vector Machines
• Functional Classification in Hilbert Spaces
• Consistency of kernel-based quantile regression
• Regression Quantiles
• On Different Facets of Regularization Theory
• Nonparametric models for functional data, with application in regression, time series prediction and curve discrimination
• Quantile Regression in Reproducing Kernel Hilbert Spaces
• Probability Inequalities for Sums of Bounded Random Variables

This page was built for publication: Weak consistency of the support vector machine quantile regression approach when covariates are functions