CHARLIER, Isabelle; PAINDAVEINE, Davy; SARACCO, Jérôme

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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]

PAINDAVEINE, Davy
European Center for Advanced Research in Economics and Statistics [ECARES]
Département de Mathématique [Bruxelles] [ULB]

SARACCO, Jérôme
Institut de Mathématiques de Bordeaux [IMB]
Quality control and dynamic reliability [CQFD]

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Ce document a été publié dans

International Conference on Computational Statistics (COMPSTAT'2014), 2014-08-19, Genève.

Résumé en anglais

We construct a nonparametric estimator of conditional quantiles of Y given X = x using optimal quantization. Conditional quantiles are particularly of interest when the condi-tional mean is not representative of the impact of the covariable X on the dependent variable Y . L p -norm optimal quantization is a discretizing method used since the 1950's in engineer-ing. It allows to construct the best approximation of a continuous law with a discrete law with support of size N . The aim of this work is then to use optimal quantization to construct con-ditional quantile estimators. We study the convergence of the approximation (N → ∞) and the consistency of the resulting estimator for this fixed-N approximation. This estimator was implemented in R in order to evaluate the numerical behavior and to compare it with existing methods.< Réduire

Mots clés en anglais

Nonparametric estimation

Conditional quantile

Optimal quantization

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Conditional quantile estimation using optimal quantization: a numerical study

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