A note on wavelet density deconvolution for weakly dependent data

In this paper we investigate the performance of a linear wavelet-type deconvolution estimator for weakly dependent data. We show that the rates of convergence which are optimal in the case of i.i.d. data are also (almost) attained for strongly mixing observations, provided the mixing coefficients de...

Full description

Saved in:

Bibliographic Details
Published in:	Statistical inference for stochastic processes : an international journal devoted to time series analysis and the statistics of continuous time processes and dynamic systems Vol. 11; no. 2; pp. 207 - 219
Main Authors:	van Zanten, Harry, Zareba, Pawel
Format:	Journal Article
Language:	English
Published:	Dordrecht Springer Netherlands 01-06-2008
Subjects:	Mathematics Mathematics and Statistics Probability Theory and Stochastic Processes Statistical Theory and Methods Stochastic volatility model Nonparametric deconvolution Rate of convergence Strong mixing
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	In this paper we investigate the performance of a linear wavelet-type deconvolution estimator for weakly dependent data. We show that the rates of convergence which are optimal in the case of i.i.d. data are also (almost) attained for strongly mixing observations, provided the mixing coefficients decay fast enough. The results are applied to a discretely observed continuous-time stochastic volatility model.
ISSN:	1387-0874 1572-9311
DOI:	10.1007/s11203-007-9013-0