A note on wavelet density deconvolution for weakly dependent data

J.H. Zanten, van, P. Zareba

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    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.
    Original languageEnglish
    Pages (from-to)207-219
    JournalStatistical Inference for Stochastic Processes
    Volume11
    Issue number2
    DOIs
    Publication statusPublished - 2008

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