dc.contributor.advisor Walker, Stephen G., 1945- en dc.creator Wang, Mengjie en dc.date.accessioned 2015-11-16T18:37:10Z en dc.date.available 2015-11-16T18:37:10Z en dc.date.issued 2015-05 en dc.date.submitted May 2015 en dc.identifier doi:10.15781/T2WK81 en dc.identifier.uri http://hdl.handle.net/2152/32496 en dc.description text en dc.description.abstract There 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.mimetype application/pdf en dc.language.iso en en dc.subject Model selection en dc.subject Bayes factor en dc.subject BIC en dc.subject Zellner's g prior en dc.subject Type I error en dc.title Assigning g in Zellner's g prior for Bayesian variable selection en dc.type Thesis en dc.date.updated 2015-11-16T18:37:10Z en dc.contributor.committeeMember Lin, Lizhen en dc.description.department Statistics en thesis.degree.department Statistics en thesis.degree.discipline Statistics en thesis.degree.grantor The University of Texas at Austin en thesis.degree.level Masters en thesis.degree.name Master of Science in Statistics en
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