Propriétés asymptotiques d'estimateurs convergents des quantiles conditionnels

Alain Berlinet; Ali Gannoun; Eric Matzner-Løber

doi:10.1016/S0764-4442(98)85016-7

Propriétés asymptotiques d'estimateurs convergents des quantiles conditionnels

Alain Berlinet
, Ali Gannoun
, Eric Matzner-Løber

University of Montpellier (UMR MiVEGEC)

Résultats de recherche: Contribution à un journal › Article › Revue par des pairs

Résumé

We state sufficient conditions for asymptotic normality of convergent estimates of the conditional quantiles, irrespective of data dependence, and give an application to α-mixing stationary processes, under optimal conditions. As an application, we use asymptotic normality to construct confidence bands for predictors based on nonparametric estimates of the conditional median.

Titre traduit de la contribution	Asymptotic properties of convergent estimators of conditional quantiles
langue originale	Français
Pages (de - à)	611-614
Nombre de pages	4
journal	Comptes Rendus de l'Academie des Sciences - Series I: Mathematics
Volume	326
Numéro de publication	5
Les DOIs	https://doi.org/10.1016/S0764-4442(98)85016-7
état	Publié - 1 janv. 1998
Modification externe	Oui

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10.1016/S0764-4442(98)85016-7

Contient cette citation

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title = "Propri{\'e}t{\'e}s asymptotiques d'estimateurs convergents des quantiles conditionnels",

abstract = "We state sufficient conditions for asymptotic normality of convergent estimates of the conditional quantiles, irrespective of data dependence, and give an application to α-mixing stationary processes, under optimal conditions. As an application, we use asymptotic normality to construct confidence bands for predictors based on nonparametric estimates of the conditional median.",

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AU - Gannoun, Ali

AU - Matzner-Løber, Eric

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N2 - We state sufficient conditions for asymptotic normality of convergent estimates of the conditional quantiles, irrespective of data dependence, and give an application to α-mixing stationary processes, under optimal conditions. As an application, we use asymptotic normality to construct confidence bands for predictors based on nonparametric estimates of the conditional median.

AB - We state sufficient conditions for asymptotic normality of convergent estimates of the conditional quantiles, irrespective of data dependence, and give an application to α-mixing stationary processes, under optimal conditions. As an application, we use asymptotic normality to construct confidence bands for predictors based on nonparametric estimates of the conditional median.

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DO - 10.1016/S0764-4442(98)85016-7

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Propriétés asymptotiques d'estimateurs convergents des quantiles conditionnels

Résumé

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