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Assessment of Vague and Noninformative Priors for Bayesian Estimation of the Realized Random Effects in Random Effects Meta-Analysis
Physikalisch-Technische Bundesanstalt, Germany. (MAM)
Physikalisch-Technische Bundesanstalt, Germany.
2018 (English)In: AStA Advances in Statistical Analysis, ISSN 1863-8171, E-ISSN 1863-818X, Vol. 102, p. 1-20Article in journal (Refereed) Published
Abstract [en]

Random-effects meta-analysis has become a well-established tool applied in many areas, for example, when combining the results of several clinical studies on a treatment effect. Typically, the inference aims at the common mean and the amount of heterogeneity. In some applications, the laboratory effects are of interest, for example, when assessing uncertainties quoted by laboratories participating in an interlaboratory comparison in metrology. We consider the Bayesian estimation of the realized random effects in random-effects meta-analysis. Several vague and noninformative priors are examined as well as a proposed novel one. Conditions are established that ensure propriety of the posteriors for the realized random effects. We present extensive simulation results that assess the inference in dependence on the choice of prior as well as mis-specifications in the statistical model. Overall good performance is observed for all priors with the novel prior showing the most promising results. Finally, the uncertainties reported by eleven national metrology institutes and universities for their measurements on the Newtonian constant of gravitation are assessed.

Place, publisher, year, edition, pages
2018. Vol. 102, p. 1-20
Keywords [en]
Random-effects model, Bayesian estimation, Reference prior, Newtonian constant of gravitation
National Category
Probability Theory and Statistics
Research subject
Mathematics/Applied Mathematics
Identifiers
URN: urn:nbn:se:mdh:diva-41064DOI: 10.1007/s10182-016-0279-7Scopus ID: 2-s2.0-84992738252OAI: oai:DiVA.org:mdh-41064DiVA, id: diva2:1252010
Available from: 2018-09-28 Created: 2018-09-28 Last updated: 2018-10-01Bibliographically approved

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Bodnar, Olha
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