The Annals of Statistics

Effect of mean on variance function estimation in nonparametric regression

Lie Wang, Lawrence D. Brown, T. Tony Cai, and Michael Levine

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Variance function estimation in nonparametric regression is considered and the minimax rate of convergence is derived. We are particularly interested in the effect of the unknown mean on the estimation of the variance function. Our results indicate that, contrary to the common practice, it is not desirable to base the estimator of the variance function on the residuals from an optimal estimator of the mean when the mean function is not smooth. Instead it is more desirable to use estimators of the mean with minimal bias. On the other hand, when the mean function is very smooth, our numerical results show that the residual-based method performs better, but not substantial better than the first-order-difference-based estimator. In addition our asymptotic results also correct the optimal rate claimed in Hall and Carroll [J. Roy. Statist. Soc. Ser. B 51 (1989) 3–14].

Article information

Ann. Statist., Volume 36, Number 2 (2008), 646-664.

First available in Project Euclid: 13 March 2008

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Zentralblatt MATH identifier

Primary: 62G08: Nonparametric regression 62G20: Asymptotic properties

Minimax estimation nonparametric regression variance estimation


Wang, Lie; Brown, Lawrence D.; Cai, T. Tony; Levine, Michael. Effect of mean on variance function estimation in nonparametric regression. Ann. Statist. 36 (2008), no. 2, 646--664. doi:10.1214/009053607000000901.

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