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2014 A Least Squares Method for Variance Estimation in Heteroscedastic Nonparametric Regression
Yuejin Zhou, Yebin Cheng, Tiejun Tong
J. Appl. Math. 2014: 1-14 (2014). DOI: 10.1155/2014/585146

Abstract

Interest in variance estimation in nonparametric regression has grown greatly in the past several decades. Among the existing methods, the least squares estimator in Tong and Wang (2005) is shown to have nice statistical properties and is also easy to implement. Nevertheless, their method only applies to regression models with homoscedastic errors. In this paper, we propose two least squares estimators for the error variance in heteroscedastic nonparametric regression: the intercept estimator and the slope estimator. Both estimators are shown to be consistent and their asymptotic properties are investigated. Finally, we demonstrate through simulation studies that the proposed estimators perform better than the existing competitor in various settings.

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Yuejin Zhou. Yebin Cheng. Tiejun Tong. "A Least Squares Method for Variance Estimation in Heteroscedastic Nonparametric Regression." J. Appl. Math. 2014 1 - 14, 2014. https://doi.org/10.1155/2014/585146

Information

Published: 2014
First available in Project Euclid: 2 March 2015

zbMATH: 07131709
MathSciNet: MR3230576
Digital Object Identifier: 10.1155/2014/585146

Rights: Copyright © 2014 Hindawi

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