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dc.contributor.authorChakraborty, Sounakeng
dc.contributor.authorMallick, Bani K., 1965-eng
dc.contributor.authorGhosh, Debashiseng
dc.contributor.authorGhosh, Malayeng
dc.contributor.authorDougherty, Edwardeng
dc.contributor.otherUniversity of Missouri-Columbia. College of Arts and Sciences. Department of Statisticseng
dc.date.issued2007eng
dc.description.abstractThis paper considers several Bayesian classification methods for the analysis of the glioma cancer with microarray data based on reproducing kernel Hilbert space under the multiclass setup. We consider the multinomial logit likelihood as well as the likelihood related to the multiclass Support Vector Machine (SVM) model. It is shown that our proposed Bayesian classification models with multiple shrinkage parameters can produce more accurate classification scheme for the glioma cancer compared to several existing classical methods. We have also proposed a Bayesian variable selection scheme for selecting the differentially expressed genes integrated with our model. This integrated approach improves classifier design by yielding simultaneous gene selection.eng
dc.identifier.citationSankhya : The Indian Journal of Statistics, Volume 69, Part 3, pp. 514-547.eng
dc.identifier.urihttp://hdl.handle.net/10355/9123eng
dc.publisherIndian Statistical Instituteeng
dc.relation.ispartofStatistics publications (MU)eng
dc.subjectGibbs samplingeng
dc.subjectmicroarrayseng
dc.subjectsupport vector machineseng
dc.subject.lcshGene expressioneng
dc.subject.lcshBayesian statistical decision theoryeng
dc.subject.lcshGliomas -- Statistical methodseng
dc.subject.lcshBrain -- Tumors -- Classificationeng
dc.titleGene Expression-Based Glioma Classification Using Hierarchical Bayesian Vector Machineseng
dc.typeArticleeng


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