Zhou, Haiming H.
Ph.D. (Doctor of Philosophy)
Department of Mathematical Sciences
Zellner's objective g-prior has been widely used in linear regression models due to its simple interpretation and computational tractability in evaluating marginal likelihoods. However, the g-prior further allows portioning the prior variability explained by the linear predictor versus that of pure noise. Here, a novel, yet remarkably simple g-prior speci_cation is proposed when a subject-matter expert has information on the marginal distribution of the response yi. The approach is extended for use in mixed models with some surprising, but intuitive results. Also, this formulation of g-prior is compared with other approaches via simulation studies.
Chien, Yu-Fang, "informative G-Prior for Linear Models" (2022). Graduate Research Theses & Dissertations. 7135.
Northern Illinois University
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