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

被引：14

作者：

Crambes, Christophe ^{[1
]}

Gannoun, Ali ^{[1
]}

Henchiri, Yousri ^{[1
]}

机构：

[1] Univ Montpellier 2, Inst Math & Modelisat Montpellier, CNRS, UMR 5149,Equipe Probabilites & Stat, F-34095 Montpellier, France

来源：

JOURNAL OF MULTIVARIATE ANALYSIS | 2013年 / 121卷

关键词：

Conditional quantile regression; Functional covariate; Iterative reweighted least squares; Reproducing kernel Hilbert space; Support vector machine; CONSISTENCY;

D O I：

10.1016/j.jmva.2013.06.004

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

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. (C) 2013 Elsevier Inc. All rights reserved.

引用

页码：50 / 68

页数：19

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