Abstract
This paper provides an asymptotic expansion for the mean integrated squared error (MISE) of nonlinear wavelet-based mean regression function estimators with long memory data. This MISE expansion, when the underlying mean regression function is only piecewise smooth, is the same as analogous expansion for the kernel estimators. However, for the kernel estimators, this MISE expansion generally fails if the additional smoothness assumption is absent.
Similar content being viewed by others
References
Hart J D. Kernel regression estimation with time series errors[J]. J Roy Statist Soc, Ser B, 1991, 53(1):173–187.
Beran J. Statistics for Long Memory Processes[M]. Chapman and Hall, New York, 1994, 248–260.
Hall P, Hart J D. Nonparametric regression with long-range dependence[J]. Stochastic Process Appl, 1990, 36(2):339–351.
Hardle W, Kerkyacharian G, Picard D, Tsybakov A. Wavelets, Approximation and Statistical Applications[M]. Lecture Notes in Statistics, 129, Springer-Verlag, New York, 1998, 125–212.
Hall P, Patil P. Formulae for mean integated squared error of non-linear wavelet-based density estimators[J]. Ann Statist, 1995, 23(3):905–928.
Daubechies I. Ten Lectures on Wavelets[M]. SIAM, Philadelphia, 1992, 170–185.
Mojor P. Multiple Wiener-Itô Integrals[M]. Lecture Notes in Math, 849, Springer-Verlag, New York, 1981, 88–120.
Fox R, Taqqu M. Noncentral limit theorems for quadratic forms in random variables having long-range dependence[J]. Ann Probab, 1985, 13(2):428–446.
Author information
Authors and Affiliations
Corresponding author
Additional information
Communicated by ZHOU Zhe-wei
Rights and permissions
About this article
Cite this article
Li, Ly., Xiao, Ym. Wavelet-based estimators of mean regression function with long memory data. Appl Math Mech 27, 901–910 (2006). https://doi.org/10.1007/s10483-006-0705-1
Received:
Revised:
Issue Date:
DOI: https://doi.org/10.1007/s10483-006-0705-1