Prior sensitivity analysis for a hierarchical model

Title: Prior sensitivity analysis for a hierarchical model
Creator: Junaidi; Stojanovski, E.; Nur, D.
Relation: 4th Applied Statistics Education and Research Collaboration (ASEARC) Conference. Proceedings of the 4th Applied Statistics Education and Research Collaboration (ASEARC) Conference (Parramatta, NSW 17-18 February, 2011) p. 64-67
Relation: http://eis.uow.edu.au/asearc/4thAnnResCon/index.html
Publisher: University of Wollongong
Resource Type: conference paper
Date: 2011
Description: Meta-analysis can be presented in the Frequentist or Bayesian framework. Based on the model of DuMouchel, a simulation study is conducted which fixes the overall mean and variance-covariance matrix to generate estimates of the true mean effect. These estimates will be compared to the true effect to assess bias. A sensitivity analysis, to measure the robustness of results to the selection of prior distributions, is conducted by employing Uniform and Pareto distributions for the variance components, the t-distribution for the overall mean component and a combination of priors for both variance and mean components respectively. Results were more sensitive when the prior was changed only on the overall mean component.
Subject: sensitivity analysis; hierarchical Bayesian model; meta-analysis
Identifier: http://hdl.handle.net/1959.13/1051724
Identifier: uon:15309
Identifier: ISBN:9781741281958
Language: eng
Full Text
Reviewed

		Thumbnail	File	Description	Size	Format
View Details Download			ATTACHMENT01	Publisher version (open access)	293 KB	Adobe Acrobat PDF	View Details Download