CHARLIER, Isabelle; PAINDAVEINE, Davy; SARACCO, Jérôme

doi:10.1016/j.jspi.2014.08.003

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

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

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Ce document a été publié dans

Journal of Statistical Planning and Inference. 2015, vol. 156, p. 14 - 30

Elsevier

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In this paper, we use quantization to construct a nonparametric estimator of conditional quantiles of a scalar response Y given a d-dimensional vector of covariates X. First we focus on the population level and show how optimal quantization of X, which consists in discretizing X by projecting it on an appropriate grid of N points, allows to approximate conditional quantiles of Y given X. We show that this approximation is arbitrarily good as N goes to infinity and provide a rate of convergence for the approximation error. Then we turn to the sample case and define an estimator of conditional quantiles based on quantization ideas. We prove that this estimator is consistent for its fixed-N population counterpart. The results are illustrated on a numerical example. Dominance of our estimators over local constant/linear ones and nearest neighbor ones is demonstrated through extensive simulations in the companion paper Charlier et al. (2014).< Réduire

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Conditional quantile estimation through optimal quantization

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