Assessing convergence diagnostic tests for Bayesian Cox regression

dc.authoridalkan, nesrin/0000-0003-1452-4780
dc.contributor.authorAlkan, Nesrin
dc.date.accessioned2025-03-23T19:35:16Z
dc.date.available2025-03-23T19:35:16Z
dc.date.issued2017
dc.departmentSinop Üniversitesi
dc.description.abstractThe Markov chain Monte Carlo (MCMC) method generates samples from the posterior distribution and uses these samples to approximate expectations of quantities of interest. For the process, researchers have to decide whether the Markov chain has reached the desired posterior distribution. Using convergence diagnostic tests are very important to decide whether the Markov chain has reached the target distribution. Our interest in this study was to compare the performances of convergence diagnostic tests for all parameters of Bayesian Cox regression model with different number of iterations by using a simulation and a real lung cancer dataset.
dc.identifier.doi10.1080/03610918.2015.1080835
dc.identifier.endpage3212
dc.identifier.issn0361-0918
dc.identifier.issn1532-4141
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85006919192
dc.identifier.scopusqualityQ2
dc.identifier.startpage3201
dc.identifier.urihttps://doi.org/10.1080/03610918.2015.1080835
dc.identifier.urihttps://hdl.handle.net/11486/5829
dc.identifier.volume46
dc.identifier.wosWOS:000400186200046
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorAlkan, Nesrin
dc.language.isoen
dc.publisherTaylor & Francis Inc
dc.relation.ispartofCommunications in Statistics-Simulation and Computation
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250323
dc.subjectBayesian Cox regression
dc.subjectConvergence diagnostic tests
dc.subjectMarkov Chain Monte Carlo
dc.titleAssessing convergence diagnostic tests for Bayesian Cox regression
dc.typeArticle

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