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Fully Bayes Factors With a Generalized g-Prior
| Content Provider | Semantic Scholar |
|---|---|
| Author | Maruyama, Yuzo |
| Copyright Year | 2011 |
| Abstract | For the normal linear model variable selection problem, we propose selection criteria based on a fully Bayes formulation with a generalization of Zellner’s g-prior which allows for p > n. A special case of the prior formulation is seen to yield tractable closed forms for marginal densities and Bayes factors which reveal new model evaluation characteristics of potential interest. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www-stat.wharton.upenn.edu/~edgeorge/Research_papers/MG11%20AOS917.pdf |
| Alternate Webpage(s) | https://repository.upenn.edu/cgi/viewcontent.cgi?article=1394&context=statistics_papers |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Bayes factor Choice Behavior Coefficient Column (database) Feature selection Intercept Substance Population Parameter Selection algorithm Subgroup Subscript Visual Intercept |
| Content Type | Text |
| Resource Type | Article |
