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Nonparametric Quantile Regression Estimation for Functional Dependent Data
Journal article   Peer reviewed

Nonparametric Quantile Regression Estimation for Functional Dependent Data

Sophie Dabo-Niang and Ali Laksaci
Communications in statistics. Theory and methods, Vol.41(7), pp.1254-1268
01/04/2012

Abstract

Mathematics Physical Sciences Science & Technology Statistics & Probability
Let (X-i, Y-i)(i=1, ... , n) be a sequence of strongly mixing random variables valued in F x R, where F is a semi-metric space. We consider the problem of estimating the quantile regression function of Y-i given X-i. The principal aim of the article is to prove the consistency in L-p norm of the proposed kernel estimate. The usefulness of the estimation is illustrated by a real data application where we are interested in forecasting hourly ozone concentration in the south-east of French.

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