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Monte Carlo EM estimation for multivariate stable distributions

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  • Ravishanker, Nalini
  • Qiou, Zuqiang

Abstract

We describe parameter estimation for the multivariate sub-Gaussian symmetric stable distribution using Monte Carlo EM algorithm. Two augmented vectors are employed in the construction of the posterior joint density of the stable parameters. Gibbs sampling enables the generation of these vectors from their respective conditional posterior distributions and thus facilitates the expectation step of the algorithm.

Suggested Citation

  • Ravishanker, Nalini & Qiou, Zuqiang, 1999. "Monte Carlo EM estimation for multivariate stable distributions," Statistics & Probability Letters, Elsevier, vol. 45(4), pages 335-340, December.
    • Handle: RePEc:eee:stapro:v:45:y:1999:i:4:p:335-340
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    References listed on IDEAS

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    1. B. N. Cheng & S. T. Rachev, 1995. "Multivariate Stable Futures Prices," Mathematical Finance, Wiley Blackwell, vol. 5(2), pages 133-153, April.
    2. Press, S. J., 1972. "Multivariate stable distributions," Journal of Multivariate Analysis, Elsevier, vol. 2(4), pages 444-462, December.
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    Cited by:

    1. Tsionas, Mike G., 2016. "Bayesian analysis of multivariate stable distributions using one-dimensional projections," Journal of Multivariate Analysis, Elsevier, vol. 143(C), pages 185-193.
    2. Audrius Kabašinskas & Leonidas Sakalauskas & Ingrida Vaičiulytė, 2021. "An Analytical EM Algorithm for Sub-Gaussian Vectors," Mathematics, MDPI, vol. 9(9), pages 1-20, April.
    3. Ogata, Hiroaki, 2013. "Estimation for multivariate stable distributions with generalized empirical likelihood," Journal of Econometrics, Elsevier, vol. 172(2), pages 248-254.
    4. Tsionas, Mike, 2012. "Simple techniques for likelihood analysis of univariate and multivariate stable distributions: with extensions to multivariate stochastic volatility and dynamic factor models," MPRA Paper 40966, University Library of Munich, Germany, revised 20 Aug 2012.

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