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

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dc.contributor.advisor Bratcher, Thomas L.
dc.contributor.author McBride, John Jacob.
dc.contributor.other Baylor University. Dept. of Statistical Sciences. en
dc.date.copyright 2006-05
dc.identifier.uri http://hdl.handle.net/2104/3955
dc.description.abstract The 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.rights Baylor 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.subject Spatial analysis (Statistics). en
dc.subject Bayesian statistical decision theory. en
dc.title Conjugate hierarchical models for spatial data: an application on an optimal selection procedure. en
dc.type Thesis en
dc.description.degree Ph.D. en
dc.rights.accessrights Worldwide access en
dc.contributor.department Statistical Sciences. en


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