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June, 1984 A Law of the Iterated Logarithm for Nonparametric Regression Function Estimators
Wolfgang Hardle
Ann. Statist. 12(2): 624-635 (June, 1984). DOI: 10.1214/aos/1176346510

Abstract

We study the estimation of a regression function by two classes of estimators, the Nadaraya-Watson Kernel type estimators and the orthogonal polynomial estimators. We obtain sharp pointwise rates of strong consistency by establishing laws of the iterated logarithm for the two classes of estimators. These results parallel those of Hall (1981) on density estimation and extend those of Noda (1976) on strong consistency of kernel regression estimators.

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Wolfgang Hardle. "A Law of the Iterated Logarithm for Nonparametric Regression Function Estimators." Ann. Statist. 12 (2) 624 - 635, June, 1984. https://doi.org/10.1214/aos/1176346510

Information

Published: June, 1984
First available in Project Euclid: 12 April 2007

zbMATH: 0591.62030
MathSciNet: MR740916
Digital Object Identifier: 10.1214/aos/1176346510

Subjects:
Primary: 60F10
Secondary: 60G15, 62G05

Rights: Copyright © 1984 Institute of Mathematical Statistics

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Vol.12 • No. 2 • June, 1984
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