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https://elib.bsu.by/handle/123456789/94529
Полная запись метаданных
Поле DC | Значение | Язык |
---|---|---|
dc.contributor.author | Sakalauskas, L. | - |
dc.contributor.author | Vaiciulyte, L. | - |
dc.date.accessioned | 2014-04-22T07:10:39Z | - |
dc.date.available | 2014-04-22T07:10:39Z | - |
dc.date.issued | 2010 | - |
dc.identifier.uri | http://elib.bsu.by/handle/123456789/94529 | - |
dc.description.abstract | The Monte-Carlo Markov Chain (MCMC) method for estimation of skew t-distribution is developed in the paper. Using the representation of the skew t- distribution is represented by multivariate skew { normal distribution with covari- ance matrix depending on parameter distributed according to inverse { gamma distribution (Azzalini and Genton, 2008), the density of skew t-distribution is expressed through multivariate integral. Next, the MCMC procedure is con- structed for recurrent estimation of skew t-distribution by maximum likelihood, where the Monte-Carlo sample size is regulated so that to ensure the convergence and to decrease the total amount of Monte-Carlo trials. The conЇdence intervals of Monte-Carlo estimators are introduced because the asymptotic distribution of Monte-Carlo estimators is Gaussian according to the CLT and the termination rule is implemented testing statistical hypotheses about insigniЇcant change of estimates in two steps of the procedure (Sakalauskas, 2000). | ru |
dc.language.iso | en | ru |
dc.publisher | Minsk: BSU | ru |
dc.subject | ЭБ БГУ::ОБЩЕСТВЕННЫЕ НАУКИ::Информатика | ru |
dc.title | Estimation of skew t-distribution by Monte-Carlo Markov chain approach | ru |
dc.type | conference paper | ru |
Располагается в коллекциях: | Section 2. MULTIVARIATE ANALYSIS AND DESIGN OF EXPERIMENTS |
Полный текст документа:
Файл | Описание | Размер | Формат | |
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S02-SakalauskasVaiciulyte.pdf | 121,27 kB | Adobe PDF | Открыть |
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