Computationally Efficient Estimation of Non-stationary Gaussian Process Models for Large Spatial Data.

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dc.contributor.advisorMontserrat Fuentes, Co-Chair
dc.contributor.advisorJoseph Guinness, Co-Chair
dc.contributor.advisorSoumendra Lahiri, Member
dc.contributor.advisorDean Hesterberg, Member
dc.contributor.authorMuyskens, Amanda Leigh
dc.date.accepted2019-02-21
dc.date.accessioned2019-03-01T13:30:44Z
dc.date.available2019-03-01T13:30:44Z
dc.date.defense2019-02-15
dc.date.issued2019-02-15
dc.date.released2019-03-01
dc.date.reviewed2019-02-20
dc.date.submitted2019-02-20
dc.degree.disciplineStatistics
dc.degree.leveldissertation
dc.degree.nameDoctor of Philosophy
dc.descriptionNorth Carolina State University Theses Statistics.
dc.formatPh.D. North Carolina State University, 2019.
dc.identifier.otherdeg13809
dc.identifier.urihttp://www.lib.ncsu.edu/resolver/1840.20/36314
dc.rights
dc.titleComputationally Efficient Estimation of Non-stationary Gaussian Process Models for Large Spatial Data.
dcterms.extent1 online resource (xi, 87 pages) : illustrations (some color), color maps

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