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Journal Articles The R Journal Year : 2015

QuantifQuantile : an R package for performing quantile regression through optimal quantization

Abstract

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.
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Dates and versions

hal-01108505 , version 1 (22-01-2015)
hal-01108505 , version 2 (13-01-2016)

Identifiers

  • HAL Id : hal-01108505 , version 2

Cite

Isabelle Charlier, Davy Paindaveine, Jérôme Saracco. QuantifQuantile : an R package for performing quantile regression through optimal quantization. The R Journal, 2015. ⟨hal-01108505v2⟩
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