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QuantifQuantile; an R Package for Performing Quantile Regression through Optimal Quantization

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Listed:
  • Isabelle Charlier
  • Davy Paindaveine
  • Jérôme Saracco

Abstract

Quantile regression allows to assess the impact of some covariate X on a response Y .An important application is the construction of reference curves and conditional predictionintervals for Y .Recently, Charlier et al. (2014a) developed a new nonparametric quantileregression method based on the concept of optimal quantization. This method, as shownin Charlier et al. (2014b), competes very well with its classical nearest-neighbor or kernelcompetitors. In this paper, we describe an R package, called QuantifQuantile, that allowsto perform quantization-based quantile regression. We describe the various functions of thepackage and provide examples.

Suggested Citation

  • Isabelle Charlier & Davy Paindaveine & Jérôme Saracco, 2014. "QuantifQuantile; an R Package for Performing Quantile Regression through Optimal Quantization," Working Papers ECARES ECARES 2014-40, ULB -- Universite Libre de Bruxelles.
    • Handle: RePEc:eca:wpaper:2013/174930
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    References listed on IDEAS

    as
    1. Isabelle Charlier & Davy Paindaveine, 2014. "Conditional Quantile Estimation through Optimal Quantization," Working Papers ECARES ECARES 2014-28, ULB -- Universite Libre de Bruxelles.
    2. Isabelle Charlier & Davy Paindaveine & Jérôme Saracco, 2014. "Conditional Quantile Estimation Based on Optimal Quantization: from Theory to Practice," Working Papers ECARES ECARES 2014-39, ULB -- Universite Libre de Bruxelles.
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    1. Isabelle Charlier & Davy Paindaveine & Jérôme Saracco, 2016. "Multiple-Output Quantile Regression through Optimal Quantization," Working Papers ECARES ECARES 2016-18, ULB -- Universite Libre de Bruxelles.
    2. Charlier, Isabelle & Paindaveine, Davy & Saracco, Jérôme, 2015. "Conditional quantile estimation based on optimal quantization: From theory to practice," Computational Statistics & Data Analysis, Elsevier, vol. 91(C), pages 20-39.
    3. Isabelle Charlier & Davy Paindaveine & Jérôme Saracco, 2014. "Conditional Quantile Estimation Based on Optimal Quantization: from Theory to Practice," Working Papers ECARES ECARES 2014-39, ULB -- Universite Libre de Bruxelles.

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