Date of this Version
The Annals of Statistics
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].
minimax estimation, nonparametric regression, variance estimation
Wang, L., Brown, L. D., Cai, T., & Levine, M. (2008). Effect of Mean on Variance Function Estimation in Nonparametric Regression. The Annals of Statistics, 36 (2), 646-664. http://dx.doi.org/10.1214/009053607000000901
Date Posted: 27 November 2017
This document has been peer reviewed.