Conditional quantile estimation using optimal quantization: a numerical study - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2014

Conditional quantile estimation using optimal quantization: a numerical study

Abstract

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.
Fichier principal
Vignette du fichier
CharlierIsabellesubmission.pdf (384.16 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01109009 , version 1 (27-01-2015)

Identifiers

  • HAL Id : hal-01109009 , version 1

Cite

Isabelle Charlier, Davy Paindaveine, Jérôme Saracco. Conditional quantile estimation using optimal quantization: a numerical study. International Conference on Computational Statistics (COMPSTAT'2014), Aug 2014, Genève, Switzerland. ⟨hal-01109009⟩
233 View
75 Download

Share

Gmail Facebook X LinkedIn More