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