Kernel estimates of functions and their derivatives with applications

The problem of the estimation of functions and their derivatives from noisy observations is considered. The new kernel estimate is shown to be consistent in the mean square sense and an exact bound on the uniform mean squared error is given. An application to the system identification is discussed.

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Year of publication:	1984
Authors:	Georgiev, Alexander A.
Published in:	Statistics & Probability Letters. - Elsevier, ISSN 0167-7152. - Vol. 2.1984, 1, p. 45-50
Publisher:	Elsevier
Keywords:	regression function derivatives of regression function kernel estimation curve fitting system identification Lipschitz function mean square error convergence

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

Persistent link: https://www.econbiz.de/10005259006