ESTIMATION OF A SUB-GAUSSIAN VECTOR DISTRIBUTION BY THE MONTE-CARLO MARKOV CHAIN APPROACH
Journal Title: Jaunųjų mokslininkų darbai - Year 2014, Vol 41, Issue 1
Abstract
The Monte-Carlo Markov chain procedure for estimation of a sub-Gaussian vector distribution by the maximal likelihood method, where the Monte-Carlo sample size is regulated to ensure the convergence, is constructed in the paper. The termination rule is also implemented by testing statistical hypotheses on an insignificant change of estimates in two steps of the procedure. The termination statistic is approximated with a Chi qaudrat Monte-Carlo Markov chain approach.
Authors and Affiliations
Leonidas Sakalauskas, Ingrida Vaičiulytė
Keywords
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- EP ID EP106298
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