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:: Volume 4, Issue 1 (9-2010) ::
J. of Stat. Sci. 2010, 4(1): 35-58 Back to browse issues page
Improving of Structured Markov Chain Monte Carlo Algorithm in Multilevel Models
Atefeh Farokhy, Mousa Golalizadeh
Abstract:   (19066 Views)
The multilevel models are used in applied sciences including social sciences, sociology, medicine, economic for analysing correlated data. There are various approaches to estimate the model parameters when the responses are normally distributed. To implement the Bayesian approach, a generalized version of the Markov Chain Monte Carlo algorithm, which has a simple structure and removes the correlations among the simulated samples for the fixed parameters and the errors in higher levels, is used in this article. Because the dimension of the covariance matrix for the new error vector is increased, based upon the Cholesky decomposition of the covariance matrix, two methods are proposed to speed the convergence of this approach. Then, the performances of these methods are evaluated in a simulation study and real life data.
Keywords: Multilevel Data, Random Intercept Models, MCMC Algorithm, Cholesky Decomposition.
Full-Text [PDF 2123 kb]   (3431 Downloads)    
Type of Study: Applied | Subject: Applied Statistics
Received: 2012/01/4 | Accepted: 2012/01/5 | Published: 2015/06/17
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Farokhy A, Golalizadeh M. Improving of Structured Markov Chain Monte Carlo Algorithm in Multilevel Models. J. of Stat. Sci.. 2010; 4 (1) :35-58
URL: http://jss.irstat.ir/article-1-94-en.html

Volume 4, Issue 1 (9-2010) Back to browse issues page
مجله علوم آماری – نشریه علمی پژوهشی انجمن آمار ایران Journal of Statistical Sciences
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