Estimation of the Asymptotic Variance of Kernel Density Estimators for Continuous Time Processes

In order to construct confidence sets for a marginal density f of a strictly stationary continuous time process observed over the time interval [0,Â T], it is necessary to have at one's disposal a Central Limit Theorem for the kernel density estimator fT. In this paper we address the question of nonparametric estimation of the asymptotic variance of Â fT, an unknown quantity dependent on f. We construct two estimators and study their asymptotic properties.

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Year of publication:	2001
Authors:	Guillou, Armelle ; Merlevède, Florence
Published in:	Journal of Multivariate Analysis. - Elsevier, ISSN 0047-259X. - Vol. 79.2001, 1, p. 114-137
Publisher:	Elsevier
Subject:	kernel estimator \| continuous processes strong mixing sequences confidence sets

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Type of publication:	Article
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10005160495