QuantifQuantile : an R package for performing quantile regression through optimal quantization
CHARLIER, Isabelle
Institut de Mathématiques de Bordeaux [IMB]
Quality control and dynamic reliability [CQFD]
European Center for Advanced Research in Economics and Statistics [ECARES]
Département de Mathématique [Bruxelles] [ULB]
Institut de Mathématiques de Bordeaux [IMB]
Quality control and dynamic reliability [CQFD]
European Center for Advanced Research in Economics and Statistics [ECARES]
Département de Mathématique [Bruxelles] [ULB]
PAINDAVEINE, Davy
Département de Mathématique [Bruxelles] [ULB]
European Center for Advanced Research in Economics and Statistics [ECARES]
Département de Mathématique [Bruxelles] [ULB]
European Center for Advanced Research in Economics and Statistics [ECARES]
SARACCO, Jérôme
Quality control and dynamic reliability [CQFD]
Institut de Mathématiques de Bordeaux [IMB]
Quality control and dynamic reliability [CQFD]
Institut de Mathématiques de Bordeaux [IMB]
CHARLIER, Isabelle
Institut de Mathématiques de Bordeaux [IMB]
Quality control and dynamic reliability [CQFD]
European Center for Advanced Research in Economics and Statistics [ECARES]
Département de Mathématique [Bruxelles] [ULB]
Institut de Mathématiques de Bordeaux [IMB]
Quality control and dynamic reliability [CQFD]
European Center for Advanced Research in Economics and Statistics [ECARES]
Département de Mathématique [Bruxelles] [ULB]
PAINDAVEINE, Davy
Département de Mathématique [Bruxelles] [ULB]
European Center for Advanced Research in Economics and Statistics [ECARES]
Département de Mathématique [Bruxelles] [ULB]
European Center for Advanced Research in Economics and Statistics [ECARES]
SARACCO, Jérôme
Quality control and dynamic reliability [CQFD]
Institut de Mathématiques de Bordeaux [IMB]
< Réduire
Quality control and dynamic reliability [CQFD]
Institut de Mathématiques de Bordeaux [IMB]
Langue
en
Article de revue
Ce document a été publié dans
The R Journal. 2015
R Foundation for Statistical Computing
Résumé en anglais
In quantile regression, various quantiles of a response variable Y are modelled as func- tions of covariates (rather than its mean). An important application is the construction of reference curves/surfaces and conditional ...Lire la suite >
In quantile regression, various quantiles of a response variable Y are modelled as func- tions of covariates (rather than its mean). An important application is the construction of reference curves/surfaces and conditional prediction intervals for Y. Recently, a nonparametric quantile regression method based on the concept of optimal quantization was proposed. This method competes very well with k-nearest neighbor, kernel, and spline methods. In this paper, we describe an R package, called QuantifQuantile, that allows to perform quantization-based quantile regression. We describe the various functions of the package and provide examples.< Réduire
Mots clés en anglais
quantile regression
optimal quantization
R package
Nonparametric estimation
Origine
Importé de halUnités de recherche