Date of this Version
Annals of Statistics
We consider a wavelet thresholding approach to adaptive variance function estimation in heteroscedastic nonparametric regression. A data-driven estimator is constructed by applying wavelet thresholding to the squared first-order differences of the observations. We show that the variance function estimator is nearly optimally adaptive to the smoothness of both the mean and variance functions. The estimator is shown to achieve the optimal adaptive rate of convergence under the pointwise squared error simultaneously over a range of smoothness classes. The estimator is also adaptively within a logarithmic factor of the minimax risk under the global mean integrated squared error over a collection of spatially inhomogeneous function classes. Numerical implementation and simulation results are also discussed.
Adaptive estimation, nonparametric regression, thresholding, variance function estimation, wavelets
Cai, T., & Wang, L. (2008). Adaptive Variance Function Estimation in Heteroscedastic Nonparametric Regression. Annals of Statistics, 36 (5), 2025-2054. http://dx.doi.org/10.1214/07-AOS509
Date Posted: 27 November 2017
This document has been peer reviewed.