Bayesian Methodology for Missing Data, Model Selection and Hierarchical Spatial Models with Application to Ecological Data

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dc.contributor.authorBoone, Edward L.en
dc.contributor.committeechairYe, Keyingen
dc.contributor.committeememberAnderson-Cook, Christine M.en
dc.contributor.committeememberTerrell, George R.en
dc.contributor.committeememberPrins, Samantha C. Batesen
dc.contributor.committeememberSmith, Eric P.en
dc.contributor.departmentStatisticsen
dc.date.accessioned2014-03-14T20:07:13Zen
dc.date.adate2003-02-14en
dc.date.available2014-03-14T20:07:13Zen
dc.date.issued2003-01-31en
dc.date.rdate2004-02-14en
dc.date.sdate2003-02-07en
dc.description.abstractEcological data is often fraught with many problems such as Missing Data and Spatial Correlation. In this dissertation we use a data set collected by the Ohio EPA as motivation for studying techniques to address these problems. The data set is concerned with the benthic health of Ohio's waterways. A new method for incorporating covariate structure and missing data mechanisms into missing data analysis is considered. This method allows us to detect relationships other popular methods do not allow. We then further extend this method into model selection. In the special case where the unobserved covariates are assumed normally distributed we use the Bayesian Model Averaging method to average the models, select the highest probability model and do variable assessment. Accuracy in calculating the posterior model probabilities using the Laplace approximation and an approximation based on the Bayesian Information Criterion (BIC) are explored. It is shown that the Laplace approximation is superior to the BIC based approximation using simulation. Finally, Hierarchical Spatial Linear Models are considered for the data and we show how to combine analysis which have spatial correlation within and between clusters.en
dc.description.degreePh. D.en
dc.identifier.otheretd-02072003-143850en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-02072003-143850/en
dc.identifier.urihttp://hdl.handle.net/10919/26141en
dc.publisherVirginia Techen
dc.relation.haspartDissertation.pdfen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectEcological Statisticsen
dc.subjectBayesian Model Averagingen
dc.subjectHierarchical Modelsen
dc.subjectMissing at Randomen
dc.titleBayesian Methodology for Missing Data, Model Selection and Hierarchical Spatial Models with Application to Ecological Dataen
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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