Please use this identifier to cite or link to this item: http://archive.cmb.ac.lk:8080/xmlui/handle/70130/5934
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dc.contributor.authorRasan, Darshika-
dc.contributor.authorSooriyarachchi, M.R.-
dc.contributor.authorPinto, Vimukthini-
dc.date.accessioned2021-09-14T06:02:12Z-
dc.date.available2021-09-14T06:02:12Z-
dc.date.issued2021-
dc.identifier.citationDarshika Karunarasan, Roshini Sooriyarachchi, and Vimukthini Pinto (2021). A comparison of Bayesian Markov chain Monte Carlo methods in a multilevel scenario. Communications in Statistics-Simulation and Computation.https://doi.org/10.1080/03610918.2021.1967985en_US
dc.identifier.urihttp://archive.cmb.ac.lk:8080/xmlui/handle/70130/5934-
dc.description.abstractMultilevel modeling is a modern approach to deal with hierarchical or a nested data structure which can assess the variability between clusters. Bayesian Markov Chain Monte Carlo (MCMC) methods of estimations are advanced methods applicable for estimating multilevel models. However, these estimation methods are not as yet tested to identify its’ performances as well as the properties associated with these estimation methods. This study targets to conduct a comparison of Bayesian MCMC methods which are developed for multilevel models where the response is normally distributed. The comparison is based upon extensive simulations and an application to a real-life dataset. The performance of Gibbs sampling (GS) and Metropolis Hastings (MH) methods are compared using a simulation study and additionally the factors which can affect the performance of both MCMC methods are identified. Practicality of these methods in real world scenario is confirmed through the application of MCMC method to a dataset. In the simulations though the Metropolis Hastings (MH) shows slightly better performance than Gibbs, there is no evidence to indicate that significant differences exist between these methods except for small samples where MH is superior. The results from the example are not as clear as from the simulations.en_US
dc.description.sponsorshipNo sponsors or fundingen_US
dc.language.isoenen_US
dc.publisherTaylor and Francisen_US
dc.subjectEstimation techniques; Goodness-of-fit; Markov Chain Monte Carlo; Multilevel modeling; Metropolis–Hastings; Gibbs Samplingen_US
dc.titleA comparison of Bayesian Markov chain Monte Carlo methods in a multilevel scenarioen_US
dc.typeArticleen_US
Appears in Collections:Department of Statistics



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