ROBUST BAYESIAN REGRESSION ANALYSIS USING RAMSAY-NOVICK DISTRIBUTED ERRORS WITH STUDENT-T PRIOR

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dc.contributor.authorKaya, Mutlu
dc.contributor.authorCankaya, Emel
dc.contributor.authorArslan, Olcay
dc.date.accessioned2025-03-23T19:26:37Z
dc.date.available2025-03-23T19:26:37Z
dc.date.issued2019
dc.departmentSinop Üniversitesi
dc.description.abstractThis paper investigates bayesian treatment of regression modelling with Ramsay-Novick (RN) distribution specifically developed for robust inferential procedures. It falls into the category of the so-called heavy-tailed distributions generally accepted as outlier resistant densities. RN is obtained by coverting the usual form of a non-robust density to a robust likelihood through the modification of its unbounded influence function. The resulting distributional form is quite complicated which is the reason for its limited applications in bayesian analyses of real problems. With the help of innovative Markov Chain Monte Carlo (MCMC) methods and softwares currently available, here we first suggested a random number generator for RN distribution. Then, we developed a robust bayesian modelling with RN distributed errors and Student-t prior. The prior with heavy-tailed properties is here chosen to provide a built-in protection against the misspecification of conflicting expert knowledge (i.e. prior robustness). This is particularly useful to avoid accusations of too much subjective bias in the prior specification. A simulation study conducted for performance assessment and a real-data application on the famously known stack loss data demonstrated that robust bayesian estimates with RN likelihood and heavy-tailed prior are robust against outliers in all directions and inaccurately specified priors.
dc.identifier.doi10.31801/cfsuasmas.441096
dc.identifier.endpage618
dc.identifier.issn1303-5991
dc.identifier.issue1
dc.identifier.scopusqualityN/A
dc.identifier.startpage602
dc.identifier.trdizinid377833
dc.identifier.urihttps://doi.org/10.31801/cfsuasmas.441096
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/377833
dc.identifier.urihttps://hdl.handle.net/11486/4743
dc.identifier.volume68
dc.identifier.wosWOS:000463698900048
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.publisherAnkara Univ, Fac Sci
dc.relation.ispartofCommunications Faculty of Sciences University of Ankara-Series A1 Mathematics and Statistics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250323
dc.subjectRobust bayesian regression
dc.subjectRamsay-Novick
dc.subjectheavy-tailed distribution
dc.subjectStudent-t prior
dc.subjectprior robustness
dc.titleROBUST BAYESIAN REGRESSION ANALYSIS USING RAMSAY-NOVICK DISTRIBUTED ERRORS WITH STUDENT-T PRIOR
dc.typeArticle

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