Exponential bounds of mean error for the kernel estimates of regression functions

Let (X, Y), (X1, Y1), ..., (Xn, Yn) be i.d.d. Rr - R-valued random vectors with EY < [infinity], and let Qn(x) be a kernel estimate of the regression function Q(x) = E(YX = x). In this paper, we establish an exponential bound of the mean deviation between Qn(x) and Q(x) given the training sample Zn = (X1, Y1, ..., Xn, Yn), under conditions as weak as possible.

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Year of publication:	1989
Authors:	Zhao, L. C.
Published in:	Journal of Multivariate Analysis. - Elsevier, ISSN 0047-259X. - Vol. 29.1989, 2, p. 260-273
Publisher:	Elsevier
Keywords:	exponential bound kernel estimate mean error regression function training sample

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

Persistent link: https://www.econbiz.de/10005152809