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dc.contributor.authorLoftus, Stephen Christopheren_US
dc.date.accessioned2015-12-26T09:01:59Z
dc.date.available2015-12-26T09:01:59Z
dc.date.issued2015-12-11en_US
dc.identifier.othervt_gsexam:6727en_US
dc.identifier.urihttp://hdl.handle.net/10919/64363
dc.description.abstractAs data collection techniques improve, oftentimes the number of covariates exceeds the number of observations. When this happens, regression models become oversaturated and, thus, inestimable. Many classical and Bayesian techniques have been designed to combat this difficulty, with various means of combating the oversaturation. However, these techniques can be tricky to implement well, difficult to interpret, and unstable. What is proposed is a technique that takes advantage of the natural clustering of variables that can often be found in biological and ecological datasets known as the omics datasests. Generally speaking, omics datasets attempt to classify host species structure or function by characterizing a group of biological molecules, such as genes (Genomics), the proteins (Proteomics), and metabolites (Metabolomics). By clustering the covariates and regressing on a single value for each cluster, the model becomes both estimable and stable. In addition, the technique can account for the variability within each cluster, allow for the inclusion of expert judgment, and provide a probability of inclusion for each cluster.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.subjectOversaturated modelen_US
dc.subjectBig dataen_US
dc.subjectVariable selectionen_US
dc.subjectData Analyticsen_US
dc.subjectBayesian methodsen_US
dc.titleOn the Use of Grouped Covariate Regression in Oversaturated Modelsen_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.committeechairHouse, Leanna L.en_US
dc.contributor.committeememberKim, Inyoungen_US
dc.contributor.committeememberLeman, Scott C.en_US
dc.contributor.committeememberBelden, Lisa Kayen_US


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