A least squares method for variance estimation in heteroscedastic nonparametric regression. (English) Zbl 1442.62090

Summary: Interest in variance estimation in nonparametric regression has grown greatly in the past several decades. Among the existing methods, the least squares estimator in [T. Tong and Y. Wang, Biometrika 92, No. 4, 821–830 (2005; Zbl 1151.62318)] 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.


62G08 Nonparametric regression and quantile regression
62G05 Nonparametric estimation
62G20 Asymptotic properties of nonparametric inference


Zbl 1151.62318
Full Text: DOI


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