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dc.contributor.authorHoegh, Andrew B.en_US
dc.date.accessioned2016-05-06T08:01:02Z
dc.date.available2016-05-06T08:01:02Z
dc.date.issued2016-05-05en_US
dc.identifier.othervt_gsexam:7356en_US
dc.identifier.urihttp://hdl.handle.net/10919/70921
dc.description.abstractData sets of increasing size and complexity require new approaches for prediction as the sheer volume of data from disparate sources inhibits joint processing and modeling. Rather modular segmentation is required, in which a set of models process (potentially overlapping) partitions of the data to independently construct predictions. This framework enables individuals models to be tailored for specific selective superiorities without concern for existing models, which provides utility in cases of segmented expertise. However, a method for fusing predictions from the collection of models is required as models may be correlated. This work details optimal principles for fusing binary predictions from a collection of models to issue a joint prediction. An efficient algorithm is introduced and compared with off the shelf methods for binary prediction. This framework is then implemented in an applied setting to predict instances of civil unrest in Central and South America. Finally, model fusion principles of a spatiotemporal nature are developed to predict civil unrest. A novel multiscale modeling is used for efficient, scalable computation for combining a set of spatiotemporal predictions.en_US
dc.format.mediumETDen_US
dc.publisherVirginia Techen_US
dc.rightsThis Item is protected by copyright and/or related rights. Some uses of this Item may be deemed fair and permitted by law even without permission from the rights holder(s), or the rights holder(s) may have licensed the work for use under certain conditions. For other uses you need to obtain permission from the rights holder(s).en_US
dc.subjectModel Fusionen_US
dc.subjectSpatiotemporal Modelingen_US
dc.subjectAreal Dataen_US
dc.subjectSequential Monte Carloen_US
dc.titlePredictive Model Fusion: A Modular Approach to Big, Unstructured Dataen_US
dc.typeDissertationen_US
dc.contributor.departmentStatisticsen_US
dc.description.degreePh. D.en_US
thesis.degree.namePh. D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineStatisticsen_US
dc.contributor.committeechairLeman, Scott C.en_US
dc.contributor.committeememberFerreira, Marco Antonio Rosaen_US
dc.contributor.committeememberRamakrishnan, Narendranen_US
dc.contributor.committeememberHigdon, Daviden_US


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