Publication Date
2022
Document Type
Dissertation/Thesis
First Advisor
Zhou, Haiming H.
Degree Name
Ph.D. (Doctor of Philosophy)
Legacy Department
Department of Mathematical Sciences
Abstract
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.
Recommended Citation
Chien, Yu-Fang, "informative G-Prior for Linear Models" (2022). Graduate Research Theses & Dissertations. 7135.
https://huskiecommons.lib.niu.edu/allgraduate-thesesdissertations/7135
Extent
71 pages
Language
eng
Publisher
Northern Illinois University
Rights Statement
In Copyright
Rights Statement 2
NIU theses are protected by copyright. They may be viewed from Huskie Commons for any purpose, but reproduction or distribution in any format is prohibited without the written permission of the authors.
Media Type
Text
