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dc.contributor.advisorTang, Xueying
dc.contributor.authorBaldwin, Drew
dc.creatorBaldwin, Drew
dc.date.accessioned2021-02-16T21:16:18Z
dc.date.available2021-02-16T21:16:18Z
dc.date.issued2020
dc.identifier.citationBaldwin, Drew. (2020). Spatiotemporal Bayesian Model of PM Levels in South Korea (Master's thesis, University of Arizona, Tucson, USA).
dc.identifier.urihttp://hdl.handle.net/10150/656758
dc.description.abstractThis paper determines the factors which significantly affect pollution in The Republic of Korea. Using data from the Korean Statistical Society, a Bayesian spatio-temporal model is fit to identify the relationship between PM 2.5 and predictor variables such as temperature, road length, traffic density, etc. Spike and slab priors are implemented to identify the subset of significant predictor variables for the final model. The model is then used to predict PM 2.5 for the 16 provinces of South Korea and compared against a baseline model which does not take spatial or temporal dependence into account. We find that the spatio-temporal model outperforms the non spatio-temporal model when using the mean squared error for model comparison.
dc.language.isoen
dc.publisherThe University of Arizona.
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction, presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
dc.subjectBayesian Statistics
dc.subjectPM 2.5
dc.subjectspatiotemporal
dc.titleSpatiotemporal Bayesian Model of PM Levels in South Korea
dc.typetext
dc.typeElectronic Thesis
thesis.degree.grantorUniversity of Arizona
thesis.degree.levelmasters
dc.contributor.committeememberHao, Ning
dc.contributor.committeememberBedrick, Edward
thesis.degree.disciplineGraduate College
thesis.degree.disciplineStatistics
thesis.degree.nameM.S.
refterms.dateFOA2021-02-16T21:16:18Z


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