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Nonparametric Quantile Regression Estimation for Functional Dependent Data

Abstract : Let (X i , Y i ) i=1,..., n be a sequence of strongly mixing random variables valued in ℱ × 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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https://hal.univ-lille3.fr/hal-00994965
Contributor : Sophie Dabo-Niang <>
Submitted on : Thursday, May 22, 2014 - 2:15:34 PM
Last modification on : Thursday, December 12, 2019 - 7:24:04 PM

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Sophie Dabo-Niang, Ali Laksaci. Nonparametric Quantile Regression Estimation for Functional Dependent Data. Communications in Statistics - Theory and Methods, Taylor & Francis, 2012, 41 (7), pp.1254-1268. ⟨10.1080/03610926.2010.542850⟩. ⟨hal-00994965⟩

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