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dc.contributor.advisorWalker, Stephen G., 1945-en
dc.creatorWang, Mengjieen
dc.date.accessioned2015-11-16T18:37:10Zen
dc.date.available2015-11-16T18:37:10Zen
dc.date.issued2015-05en
dc.date.submittedMay 2015en
dc.identifierdoi:10.15781/T2WK81en
dc.identifier.urihttp://hdl.handle.net/2152/32496en
dc.descriptiontexten
dc.description.abstractThere are numerous frequentist statistics variable selection methods such as Stepwise regression, AIC and BIC etc. In particular, the latter two criteria include a penalty term which discourages overfitting. In terms of the framework of Bayesian variable selection, a popular approach is using Bayes Factor (Kass & Raftery 1995), which also has a natural built-in penalty term (Berger & Pericchi 2001). Zellner's g prior (Zellner 1986) is a common prior for coefficients in the linear regression model due to its computational speed of analytic solutions for posterior. However, the choice of g is a problem which has attracted a lot of attention. (Zellner 1986) pointed out that if g is unknown, a prior can be introduced and g can be integrated out. One of the prior choices is Hyper-g Priors proposed by (Liang et al. 2008). Instead of proposing a prior for g, we will assign a fixed value for g based on controlling the Type I error for the test based on the Bayes factor. Since we are using Bayes factor to do model selection, the test statistic is Bayes factor. Every test comes with a Type I error, so it is reasonable to restrict this error under a critical value, which we will take as benchmark values, such as 0.1 or 0.05. This approach will automatically involve setting a value of g. Based on this idea, a fixed g can be selected, hence avoiding the need to find a prior for g.en
dc.format.mimetypeapplication/pdfen
dc.language.isoenen
dc.subjectModel selectionen
dc.subjectBayes factoren
dc.subjectBICen
dc.subjectZellner's g prioren
dc.subjectType I erroren
dc.titleAssigning g in Zellner's g prior for Bayesian variable selectionen
dc.typeThesisen
dc.date.updated2015-11-16T18:37:10Zen
dc.contributor.committeeMemberLin, Lizhenen
dc.description.departmentStatisticsen
thesis.degree.departmentStatisticsen
thesis.degree.disciplineStatisticsen
thesis.degree.grantorThe University of Texas at Austinen
thesis.degree.levelMastersen
thesis.degree.nameMaster of Science in Statisticsen


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