The Annals of Statistics

Adaptive variance function estimation in heteroscedastic nonparametric regression

T. Tony Cai and Lie Wang

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Abstract

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.

Article information

Source
Ann. Statist., Volume 36, Number 5 (2008), 2025-2054.

Dates
First available in Project Euclid: 13 October 2008

Permanent link to this document
https://projecteuclid.org/euclid.aos/1223908083

Digital Object Identifier
doi:10.1214/07-AOS509

Mathematical Reviews number (MathSciNet)
MR2458178

Zentralblatt MATH identifier
1148.62021

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

Keywords
Adaptive estimation nonparametric regression thresholding variance function estimation wavelets

Citation

Cai, T. Tony; Wang, Lie. Adaptive variance function estimation in heteroscedastic nonparametric regression. Ann. Statist. 36 (2008), no. 5, 2025--2054. doi:10.1214/07-AOS509. https://projecteuclid.org/euclid.aos/1223908083


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