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A Wavelet-Based Bayesian Approach to Regression Models with Long Memory Errors and Its Application to fMRI Data

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  • Jaesik Jeong
  • Marina Vannucci
  • Kyungduk Ko

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  • Jaesik Jeong & Marina Vannucci & Kyungduk Ko, 2013. "A Wavelet-Based Bayesian Approach to Regression Models with Long Memory Errors and Its Application to fMRI Data," Biometrics, The International Biometric Society, vol. 69(1), pages 184-196, March.
    • Handle: RePEc:bla:biomet:v:69:y:2013:i:1:p:184-196
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    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2012.01819.x
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    References listed on IDEAS

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    1. Jensen, Mark J., 2000. "An alternative maximum likelihood estimator of long-memory processes using compactly supported wavelets," Journal of Economic Dynamics and Control, Elsevier, vol. 24(3), pages 361-387, March.
    2. Cheung, Yin-Wong & Diebold, Francis X., 1994. "On maximum likelihood estimation of the differencing parameter of fractionally-integrated noise with unknown mean," Journal of Econometrics, Elsevier, vol. 62(2), pages 301-316, June.
    3. M. Vannucci & F. Corradi, 1999. "Covariance structure of wavelet coefficients: theory and models in a Bayesian perspective," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(4), pages 971-986.
    4. Stilian Stoev & Murad S. Taqqu, 2005. "Asymptotic self‐similarity and wavelet estimation for long‐range dependent fractional autoregressive integrated moving average time series with stable innovations," Journal of Time Series Analysis, Wiley Blackwell, vol. 26(2), pages 211-249, March.
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    Cited by:

    1. Jeong Hwan Kook & Michele Guindani & Linlin Zhang & Marina Vannucci, 2019. "NPBayes-fMRI: Non-parametric Bayesian General Linear Models for Single- and Multi-Subject fMRI Data," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 11(1), pages 3-21, April.

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