Asymptotic Normality for Density Kernel Estimators in Discrete and Continuous Time

In this paper, we build a central limit theorem for triangular arrays of sequences which satisfy a mild mixing condition. This result allows us to study asymptotic normality of density kernel estimators for some classes of continuous and discrete time processes.

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Year of publication:	1999
Authors:	Bosq, Denis ; Merlevède, Florence ; Peligrad, Magda
Published in:	Journal of Multivariate Analysis. - Elsevier, ISSN 0047-259X. - Vol. 68.1999, 1, p. 78-95
Publisher:	Elsevier
Keywords:	central limit theorem strongly mixing sequence triangular array Kernel estimator continuous and discrete time processes

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

Persistent link: https://www.econbiz.de/10005153021