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
Monte Carlo error estimation for multivariate Markov chains
β Scribed by Michael R. Kosorok
- Publisher
- Elsevier Science
- Year
- 2000
- Tongue
- English
- Weight
- 96 KB
- Volume
- 46
- Category
- Article
- ISSN
- 0167-7152
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β¦ Synopsis
In this paper, the conservative Monte Carlo error estimation methods and theory developed in Geyer (1992a, Statist. Sci. 7, 473-483) are extended from univariate to multivariate Markov chain applications. A small simulation study demonstrates the feasibility of the proposed estimators.
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