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On Grouped Observation Level Interaction and a Big Data Monte Carlo Sampling Algorithm

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dc.contributor.authorHu, Xinranen
dc.contributor.committeechairLeman, Scotland C.en
dc.contributor.committeememberNorth, Christopher L.en
dc.contributor.committeememberSmith, Eric P.en
dc.contributor.committeememberHouse, Leanna L.en
dc.contributor.departmentStatisticsen
dc.date.accessioned2015-01-27T09:00:24Zen
dc.date.available2015-01-27T09:00:24Zen
dc.date.issued2015-01-26en
dc.description.abstractBig Data is transforming the way we live. From medical care to social networks, data is playing a central role in various applications. As the volume and dimensionality of datasets keeps growing, designing effective data analytics algorithms emerges as an important research topic in statistics. In this dissertation, I will summarize our research on two data analytics algorithms: a visual analytics algorithm named Grouped Observation Level Interaction with Multidimensional Scaling and a big data Monte Carlo sampling algorithm named Batched Permutation Sampler. These two algorithms are designed to enhance the capability of generating meaningful insights and utilizing massive datasets, respectively.en
dc.description.degreePh. D.en
dc.format.mediumETDen
dc.identifier.othervt_gsexam:4290en
dc.identifier.urihttp://hdl.handle.net/10919/51224en
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectVisual Analyticsen
dc.subjectBig Data Monte Carlo Samplingen
dc.titleOn Grouped Observation Level Interaction and a Big Data Monte Carlo Sampling Algorithmen
dc.typeDissertationen
thesis.degree.disciplineStatisticsen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.leveldoctoralen
thesis.degree.namePh. D.en

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