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dc.contributor.authorWarr, Richard
dc.date.accessioned2010-09-10T20:17:37Z
dc.date.available2010-09-10T20:17:37Z
dc.date.issued2010-09-10
dc.date.submittedJuly 2010
dc.identifier.urihttp://hdl.handle.net/1928/11196
dc.description.abstractStatistical flowgraphs model multistate semi-Markov processes and provide a way to perform inference for these processes. This methodology provides powerful results that significantly impact the study of multistate semi-Markov processes. This dissertation extends previous work in several ways. First, by demonstrating how any "smooth" transition distribution can be incorporated into a statistical flowgraph model (SFGM), we provide a method to use popular distributions, such as the lognormal, that have not been used in the past. Next, we propose an alternate way to consider Bayesian SFGMs by showing how computation can be accomplished when the traditional methods of SFGMs fail to be computationally feasible. We demonstrate this method with a Bayesian non-parametric example. We extend flowgraph models to handle time-varying covariates using an accelerated failure time model. We also show how SFGMs can be used to make inference in multistate semi-Markov models to calculate exact likelihood functions when faced with incomplete data. Finally, we develop a goodness-of-fit criterion that is applicable to any continuous model and can be applied to SFGMs. This goodness-of-fit test criterion is general enough to be useful when dealing with censored and incomplete multistate data.en_US
dc.description.sponsorshipUnited States Air Forceen_US
dc.language.isoenen_US
dc.subjectSemi-Markoven_US
dc.subjectFlowgraph Modelsen_US
dc.subjectBayesian Statisticsen_US
dc.subjectSurvival Analysisen_US
dc.subjectReliabilityen_US
dc.subject.lcshFlowgraphs.
dc.subject.lcshMarkov processes--Mathematical models.
dc.subject.lcshLognormal distribution.
dc.subject.lcshBayesian statistical decision theory.
dc.subject.lcshFailure time data analysis.
dc.subject.lcshGoodness-of-fit tests.
dc.titleGeneralizations of the statistical flowgraph model frameworken_US
dc.typeDissertationen_US
dc.description.degreeStatisticsen_US
dc.description.levelDoctoralen_US
dc.description.departmentUniversity of New Mexico. Dept. of Mathematics and Statisticsen_US
dc.description.advisorChristensen, Ronald
dc.description.committee-memberHuzurbazar, Aparna
dc.description.committee-memberHuerta, Gabriel
dc.description.committee-memberStorlie, Curtis


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