A note on wavelet density deconvolution for weakly dependent data

J.H. van Zanten, P. Zareba

Research output: Contribution to JournalArticleAcademicpeer-review

195 Downloads (Pure)

Abstract

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. © 2007 Springer Science+Business Media B.V.
Original languageEnglish
Pages (from-to)207-219
JournalStatistical Inference for Stochastic Processes
Volume11
Issue number2
DOIs
Publication statusPublished - 2008

Bibliographical note

MR2403107

Fingerprint

Dive into the research topics of 'A note on wavelet density deconvolution for weakly dependent data'. Together they form a unique fingerprint.

Cite this