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September, 1989 Robust Nonparametric Regression Estimation for Dependent Observations
Graciela Boente, Ricardo Fraiman
Ann. Statist. 17(3): 1242-1256 (September, 1989). DOI: 10.1214/aos/1176347266

Abstract

Robust nonparametric estimators for regression and autoregression are proposed for $\varphi$- and $\alpha$-mixing processes. Two families of $M$-type robust equivariant estimators are considered: (i) estimators based on kernel methods and (ii) estimators based on $k$-nearest neighbor kernel methods. Strong consistency of both families is proved under mild conditions. For the first class the result is true under no assumptions whatsoever on the distribution of the observations.

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Graciela Boente. Ricardo Fraiman. "Robust Nonparametric Regression Estimation for Dependent Observations." Ann. Statist. 17 (3) 1242 - 1256, September, 1989. https://doi.org/10.1214/aos/1176347266

Information

Published: September, 1989
First available in Project Euclid: 12 April 2007

zbMATH: 0683.62023
MathSciNet: MR1015148
Digital Object Identifier: 10.1214/aos/1176347266

Subjects:
Primary: 62G05
Secondary: 62M10

Rights: Copyright © 1989 Institute of Mathematical Statistics

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Vol.17 • No. 3 • September, 1989
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