Statistics seminar: Prof. Haiming Zhou from NIU
Title: Informative g-priors for Linear Mixed Models
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. In this work, we propose a novel, yet remarkably simple g-prior specification when a subject-matter expert has information on the marginal distribution of the responses. The approach is extended for use in linear mixed models with some surprising, yet intuitive results. We compare this formulation of the g-prior with other approaches via simulation studies.
Bio: Dr. Zhou is an assistant professor of statistics and the director of statistical consulting services at Northern Illinois University. He earned his Ph.D. in statistics from the University of South Carolina in 2015. His research interests include Bayesian survival analysis, spatial modeling, semiparametric regression models, and informative priors. Today he will present his recent work on prior elicitation for linear mixed models.
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