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A note on wavelet density deconvolution for weakly dependent data

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  • Harry Zanten
  • Pawel Zareba

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  • Harry Zanten & Pawel Zareba, 2008. "A note on wavelet density deconvolution for weakly dependent data," Statistical Inference for Stochastic Processes, Springer, vol. 11(2), pages 207-219, June.
    • Handle: RePEc:spr:sistpr:v:11:y:2008:i:2:p:207-219
    DOI: 10.1007/s11203-007-9013-0
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    References listed on IDEAS

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    1. D. Blanke & B. Pumo, 2003. "Optimal sampling for density estimation in continuous time," Journal of Time Series Analysis, Wiley Blackwell, vol. 24(1), pages 1-23, January.
    2. Walter, Gilbert G., 1999. "Density estimation in the presence of noise," Statistics & Probability Letters, Elsevier, vol. 41(3), pages 237-246, February.
    3. Carrasco, Marine & Chen, Xiaohong, 2002. "Mixing And Moment Properties Of Various Garch And Stochastic Volatility Models," Econometric Theory, Cambridge University Press, vol. 18(1), pages 17-39, February.
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    Cited by:

    1. Van Es, Bert & Spreij, Peter, 2011. "Estimation of a multivariate stochastic volatility density by kernel deconvolution," Journal of Multivariate Analysis, Elsevier, vol. 102(3), pages 683-697, March.
    2. Toshio Honda, 2009. "Nonparametric regression for dependent data in the errors-in-variables problem," Global COE Hi-Stat Discussion Paper Series gd09-092, Institute of Economic Research, Hitotsubashi University.

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