Exponential bounds of mean error for the nearest neighbor estimates of regression functions

Let (X, Y), (X1, Y1),..., (Xn, Yn) be i.i.d. (Rr - R)-valued random vectors with EY < [infinity], and let mn(x) be a nearest neighbor estimate of the regression function m(x) = E(Ys[beta]X = x). We establish an exponential bound of the mean deviation between mn(x) and m(x) given the training sample Zn = (X1, Y1,..., Xn, Yn), under the conditions as weak as possible. This is a substantial improvement on Beck's result.

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Year of publication:	1987
Authors:	Zhao, L. C.
Published in:	Journal of Multivariate Analysis. - Elsevier, ISSN 0047-259X. - Vol. 21.1987, 1, p. 168-178
Publisher:	Elsevier
Keywords:	Regression function nearest neighbor estimate exponential bound mean error training sample

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

Persistent link: https://www.econbiz.de/10005093872