CHARLIER, Isabelle; PAINDAVEINE, Davy; SARACCO, Jérôme

doi:10.1016/j.jspi.2014.08.003

Metadatos

Licencia de uso del documento

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]

Idioma

Article de revue

Este ítem está publicado en

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

Elsevier

Resumen en inglés

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).< Leer menos

Metadatos

Compartir este ítem

Licencia de uso del documento

Conditional quantile estimation through optimal quantization

Idioma

Este ítem está publicado en

Resumen en inglés

URI

DOI

Orígen

Centros de investigación