On the asymptotic normality of kernel density estimators for causal linear random fields

We establish sufficient conditions for the asymptotic normality of kernel density estimators applied to causal linear random fields, by m-dependent approximation. Our conditions on the coefficients of linear random fields are weaker than the known results, although our assumption on the bandwidth is not minimal. We also establish a convergence rate of Berry–Esseen’s type.

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Year of publication:	2014
Authors:	Wang, Yizao ; Woodroofe, Michael
Published in:	Journal of Multivariate Analysis. - Elsevier, ISSN 0047-259X. - Vol. 123.2014, C, p. 201-213
Publisher:	Elsevier
Subject:	Central limit theorem \| Causal linear random field \| Kernel density estimation \| m-dependence \| Moment inequality

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

Persistent link: https://www.econbiz.de/10010718993