Conjugate hierarchical models for spatial data: an application on an optimal selection procedure.

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dc.contributor.advisorBratcher, Thomas L.
dc.contributor.authorMcBride, John Jacob.
dc.contributor.departmentStatistical Sciences.en
dc.contributor.otherBaylor University. Dept. of Statistical Sciences.en
dc.date.accessioned2006-07-24T15:25:43Z
dc.date.available2006-07-24T15:25:43Z
dc.date.copyright2006-05
dc.date.issued2006-07-24T15:25:43Z
dc.descriptionIncludes bibliographical references (p. 77-81).en
dc.description.abstractThe theory of generalized linear models provides a unifying class of statistical distributions that can be used to model both discrete and continuous events. In this dissertation we present a new conjugate hierarchical Bayesian generalized linear model that can be used to model counts of occurrences in the presence of spatial correlation. We assume that the counts are taken from geographic regions or areal units (zip codes, counties, etc.) and that the conditional distributions of these counts for each area are distributed as Poisson having unknown rates or relative risks. We incorporate the spatial association of the counts through a neighborhood structure which is based on the arrangement of the areal units. Having defined the neighborhood structure we then model this spatial association with a conditionally autoregressive (CAR) model as developed by Besag (1974). Once the spatial model has been created we adapt a subset selection procedure created by Bratcher and Bhalla (1974) to select the areal unit(s) having the highest relative risks.en
dc.description.degreePh.D.en
dc.description.statementofresponsibilityby John Jacob McBride.en
dc.format.extentviii, 81 p. : maps.en
dc.format.extent452644 bytes
dc.format.extent3298376 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/2104/3955
dc.language.isoen_USen
dc.rightsBaylor University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. Contact librarywebmaster@baylor.edu for inquiries about permission.en
dc.rights.accessrightsWorldwide accessen
dc.subjectSpatial analysis (Statistics).en
dc.subjectBayesian statistical decision theory.en
dc.titleConjugate hierarchical models for spatial data: an application on an optimal selection procedure.en
dc.typeThesisen

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