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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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Submitted on : Wednesday, January 13, 2016 - 6:06:07 PM
Last modification on : Wednesday, February 2, 2022 - 3:54:16 PM
Long-term archiving on: : Friday, November 11, 2016 - 4:59:29 AM


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  • HAL Id : hal-01108505, version 2



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



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