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Conditional Quantile Estimation Based on Optimal Quantization: from Theory to Practice

Author

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  • Isabelle Charlier
  • Davy Paindaveine
  • Jérôme Saracco

Abstract

Charlier, Paindaveine, and Saracco (2014) recently introduced a nonparametric estimatorof conditional quantiles based on optimal quantization, but almost exclusively focused onits theoretical properties. In this paper, (i) we discuss its practical implementation (byproposing in particular a method to properly select the corresponding smoothing parameter,namely the number of quantizers) and (ii) we investigate how its finite-sample performancescompare with those of its classical kernel or nearest-neighbor competitors. Monte Carlostudies reveal that the quantization-based estimator competes well in all cases and tends todominate its competitors for non-uniformly distributed covariates. We also treat a real dataset. While the paper mostly focuses on the case of a univariate covariate, we also brieflydiscuss the multivariate case and provide an illustration for bivariate regressors.

Suggested Citation

  • 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.
    • Handle: RePEc:eca:wpaper:2013/174929
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    File URL: https://dipot.ulb.ac.be/dspace/bitstream/2013/174929/1/2014-39-CHARLIER_PAINDAVEINE_SARACCO-conditional.pdf
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    References listed on IDEAS

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    1. Isabelle Charlier & Davy Paindaveine, 2014. "Conditional Quantile Estimation through Optimal Quantization," Working Papers ECARES ECARES 2014-28, ULB -- Universite Libre de Bruxelles.
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    Cited by:

    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. 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.

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    1. 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.
    2. 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.
    3. 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.

    More about this item

    Keywords

    conditional quantiles; optimal quantization; nonparametric regression;
    All these keywords.

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