Accéder directement au contenu Accéder directement à la navigation
Article dans une revue

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.
Liste complète des métadonnées

https://hal.univ-lille.fr/hal-00994965
Contributeur : Sophie Dabo-Niang <>
Soumis le : jeudi 22 mai 2014 - 14:15:34
Dernière modification le : mercredi 1 juillet 2020 - 03:22:13

Identifiants

Collections

Citation

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⟩

Partager

Métriques

Consultations de la notice

164