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dc.contributor.advisorChairperson, Graduate Committee: Steve Cherryen
dc.contributor.authorBanner, Katharine Michelleen
dc.contributor.otherMegan Higgs was a co-author of the article, 'Considerations for assessing model averaging of regression coefficients' in the journal 'Ecological applications' which is contained within this thesis.en
dc.contributor.otherMegan Higgs was a co-author of the article, 'Investigating the posterior variance of partial regression coefficients resulting from three common methods for multimodel inference' submitted to the journal 'Annals of applied statistics' which is contained within this thesis.en
dc.contributor.otherMegan Higgs was a co-author of the article, 'The model averaged posteriors plot MAPP package for R statistical software' submitted to the journal 'Journal of statistical software' which is contained within this thesis.en
dc.date.accessioned2017-05-02T19:49:47Z
dc.date.available2017-05-02T19:49:47Z
dc.date.issued2016en
dc.identifier.urihttps://scholarworks.montana.edu/xmlui/handle/1/12361
dc.description.abstractModel averaging (MA) was developed as a way to combine predictions from many models, with the goal of reducing bias and incorporating model uncertainty into final predictive inferences. A new flavor of MA, focused on averaging partial regression coefficients over multiple models, has gained traction in fields such as Ecology, Biology, and Political Science, with motivation stemming from the concern that inferences based on a single model are too 'naive' (i.e., do not fairly reflect sources of substantial uncertainty). However, coefficients appearing in multiple models do not necessarily hold the same interpretation across models, and averaging over them has the potential to result in inferences that are difficult to interpret. A gap exists between the theoretical development of MA and its current use in practice, potentially leaving well-intentioned researchers with unclear inferences, or difficulties justifying decisions to use (or not use) MA. Furthermore, it is questionable whether the perceived benefit of accounting for an additional source of uncertainty is realized in terms of increased variance for quantities of interest. In this work, we revisit relevant foundations of regression modeling, suggest more explicit notation and graphical tools, and discuss how individual model results are combined to obtain a MA result, with the goal of helping researchers make informed decisions about MA. We present a new package for R Statistical Software providing plotting functions for visualizing components going into the MA posterior distribution. This package is meant to be used to assess the implicit assumptions made by using MA for regression coefficients, complete with guidelines for use and examples. We also design and conduct a simulation study to investigate how the variance for a partial regression coefficient of interest is different for three different approaches used within multimodel inference (MA using all models, MA using a subset of models, and conditioning inferences on one model). We assess whether the perceived benefit of accounting for model uncertainty is actually realized when more models are used for final inference, with the goal of helping researches weigh tradeoffs between using variants of MA in place of one well thought out model.en
dc.language.isoenen
dc.publisherMontana State University - Bozeman, College of Letters & Scienceen
dc.subject.lcshRegression analysis.en
dc.subject.lcshMathematical models.en
dc.subject.lcshComputer software.en
dc.titleIs model averaging the solution for addressing model uncertainty? : methodological insights, tools for assessment, and considerations for practical useen
dc.typeDissertationen
dc.rights.holderCopyright 2016 by Katharine Michelle Banneren
thesis.degree.committeemembersMembers, Graduate Committee: Steve Cherry (chairperson); Megan Higgs (co-chair); Mark Greenwood; John J. Borkowski; Jennifer Green.en
thesis.degree.departmentMathematical Sciences.en
thesis.degree.genreDissertationen
thesis.degree.namePhDen
thesis.format.extentfirstpage1en
thesis.format.extentlastpage227en
mus.data.thumbpage45en


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