Consistent Estimation of the Order for Markov and Hidden Markov Chains

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dc.contributor.advisorBaras, John S.en_US
dc.contributor.authorFinesso, Lorenzoen_US
dc.contributor.departmentISRen_US
dc.date.accessioned2007-05-23T09:49:11Z
dc.date.available2007-05-23T09:49:11Z
dc.date.issued1991en_US
dc.description.abstractThe structural parameters of many statistical models can be estimated maximizing a penalized version of the likelihood function. We use this idea to construct strongly consistent estimators of the order for Markov Chains and Hidden Markov Chain models. The specification of the penalty term requires precise information on the rate of growth of the maximized likelihood ratio. For Markov chain models we determine the rate using the Law of the Iterated Logarithm. For Hidden Markov chain models we find an upper bound to the rate using results from Information Theory. We give sufficient conditions on the penalty term to avoid overestimation and underestimation of the order. Examples of penalty terms that generate strongly consitent estimators are also given.en_US
dc.format.extent5877554 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/1903/5159
dc.language.isoen_USen_US
dc.relation.ispartofseriesISR; PhD 1991-1en_US
dc.subjectestimationen_US
dc.subjectsignal processingen_US
dc.subjectstochastic systemsen_US
dc.subjectSystems Integrationen_US
dc.titleConsistent Estimation of the Order for Markov and Hidden Markov Chainsen_US
dc.typeDissertationen_US

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