Spatially Correlated Model Selection (SCOMS)

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dc.contributor.authorVelasco-Cruz, Ciroen
dc.contributor.committeecochairLeman, Scotland C.en
dc.contributor.committeecochairSmith, Eric P.en
dc.contributor.committeememberHouse, Leanna L.en
dc.contributor.committeememberGuo, Fengen
dc.contributor.departmentStatisticsen
dc.date.accessioned2014-03-14T20:12:16Zen
dc.date.adate2012-05-31en
dc.date.available2014-03-14T20:12:16Zen
dc.date.issued2012-05-04en
dc.date.rdate2012-05-31en
dc.date.sdate2012-05-18en
dc.description.abstractIn this dissertation, a variable selection method for spatial data is developed. It is assumed that the spatial process is non-stationary as a whole but is piece-wise stationary. The pieces where the spatial process is stationary are called regions. The variable selection approach accounts for two sources of correlation: (1) the spatial correlation of the data within the regions, and (2) the correlation of adjacent regions. The variable selection is carried out by including indicator variables that characterize the significance of the regression coefficients. The Ising distribution as prior for the vector of indicator variables, models the dependence of adjacent regions. We present a case study on brook trout data where the response of interest is the presence/absence of the fish at sites in the eastern United States. We find that the method outperforms the case of the probit regression where the spatial field is assumed stationary and isotropic. Additionally, the method outperformed the case where multiple regions are assumed independent of their neighbors.en
dc.description.degreePh. D.en
dc.identifier.otheretd-05182012-130009en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-05182012-130009/en
dc.identifier.urihttp://hdl.handle.net/10919/27791en
dc.publisherVirginia Techen
dc.relation.haspartVelasco-Cruz_C_D_2012.pdfen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectSpatial statisticsen
dc.subjectIsing prioren
dc.subjectNon-stationary spatial fieldsen
dc.subjectVariable Selectionen
dc.titleSpatially Correlated Model Selection (SCOMS)en
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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