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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Communication in Statistics - Theory and Methods, Taylor & Francis, 2012, 41 (7), pp.1254-1268. 〈10.1080/03610926.2010.542850〉
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https://hal.univ-lille3.fr/hal-00994965
Contributeur : Sophie Dabo-Niang <>
Soumis le : jeudi 22 mai 2014 - 14:15:34
Dernière modification le : mercredi 23 mai 2018 - 15:04:01

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

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