Bayesian Analysis for Variance Component Estimation of a Hierarchical Model: Effect of Thinning Intervals

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dc.contributor.authorNarinc, Dogan
dc.contributor.authorAygun, Ali
dc.date.accessioned2020-03-26T19:52:58Z
dc.date.available2020-03-26T19:52:58Z
dc.date.issued2018
dc.departmentSelçuk Üniversitesien_US
dc.description1ST INTERNATIONAL CONFERENCE ON MATHEMATICAL AND RELATED SCIENCES (ICMRS) -- APR 30-MAY 04, 2018 -- Antalya, TURKEYen_US
dc.description.abstractIn this study, three different X1, X2 and X3 data sets with a mean of 0, variance of 1 and 1000 samples, which were obtained by simulation and suitable for Gaussian distribution, were used. All three data sets are designed as two levels of A and B (A) in nested design. The variance components of simulated X1, X2, and X3 were assigned as sigma(2)(A):0.025, sigma(2)(B(A)):0.025, and sigma(2)(E):0.95 in X1, sigma(2)(A):0.25, sigma(2)(B(A)):0.25 and sigma(2)(E):0.50 in X2, and sigma(2)(A):0.375, sigma(2)(B(A)):0.375, and sigma(2)(E)0.25 in X3, respectively. The single chains of 200000 iterations were considered with the 20000 cycles of burn-in periods and, different thinning intervals of 18, 36, 72 cycles to result 10000, 5000, 2500 posterior samples of each parameters of interest in total for XI, X2 and X3. In result, small bias values (less than 5%) are detected only in all three chains of X3. It is revealed that the range values are well established m chains diluted from 180000 to 10000, 5000 and 2500, due to no autocorrelation was detected in any of the different variable by thinning interval combinations used in this study. Furthermore, it is important that the lowest bias values are calculated in the variable X3 where 25% of the total variance belongs to the residual. Thus, it was determined that the shares of variance components influenced estimations than thinning intervals in Bayesian analyses.en_US
dc.description.sponsorshipDuzce Univen_US
dc.identifier.doi10.1063/1.5047882en_US
dc.identifier.isbn978-0-7354-1707-6
dc.identifier.issn0094-243Xen_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://dx.doi.org/10.1063/1.5047882
dc.identifier.urihttps://hdl.handle.net/20.500.12395/36359
dc.identifier.volume1991en_US
dc.identifier.wosWOS:000450569900009en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherAMER INST PHYSICSen_US
dc.relation.ispartof1ST INTERNATIONAL CONFERENCE ON MATHEMATICAL AND RELATED SCIENCES (ICMRS 2018)en_US
dc.relation.ispartofseriesAIP Conference Proceedings
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectBayesian inferenceen_US
dc.subjectMCMCen_US
dc.subjectGibbs samplingen_US
dc.subjectThinning intervalen_US
dc.titleBayesian Analysis for Variance Component Estimation of a Hierarchical Model: Effect of Thinning Intervalsen_US
dc.typeConference Objecten_US

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