Skip to Main content Skip to Navigation
New interface
Conference papers

Estimation des quantiles conditionnels par quantification optimale : nouveaux résultats

Abstract : We construct a nonparametric estimator of conditional quantiles of Y given X using optimal quantization. Conditional quantiles are particularly of interest when it is felt that conditonal mean is not representative of the impact of the covariable X on the dependent variable Y . Optimal quantization in L p -norm is a discretizing method used since the fifties in engineering. We use it to find the best approximation of X by a discrete version with support of size N . The aim of this work is to apply optimal quantization to conditional quantile estima-tion. We study the convergence of the approximation defined above (N → ∞) and of the resulting estimator (n → ∞). It was implemented in R in order to evaluate its numerical behavior and realize a simulation study. We then compare it with existing methods.
Document type :
Conference papers
Complete list of metadata

Cited literature [6 references]  Display  Hide  Download
Contributor : Isabelle Charlier Connect in order to contact the contributor
Submitted on : Friday, January 23, 2015 - 7:42:13 PM
Last modification on : Wednesday, February 2, 2022 - 3:54:39 PM
Long-term archiving on: : Saturday, September 12, 2015 - 6:34:39 AM


Files produced by the author(s)


  • HAL Id : hal-01109003, version 1



Isabelle Charlier, Davy Paindaveine, Jérôme Saracco. Estimation des quantiles conditionnels par quantification optimale : nouveaux résultats. 46èmes Journées de Statistique, Jun 2014, Rennes, France. ⟨hal-01109003⟩



Record views


Files downloads