Show simple item record

dc.contributor.advisorLaverty, William H.en_US
dc.creatorTang, Xuanen_US
dc.date.accessioned2004-12-23T13:56:19Zen_US
dc.date.accessioned2013-01-04T05:12:44Z
dc.date.available2005-01-04T08:00:00Zen_US
dc.date.available2013-01-04T05:12:44Z
dc.date.created2004-11en_US
dc.date.issued2004-11-25en_US
dc.date.submittedNovember 2004en_US
dc.identifier.urihttp://hdl.handle.net/10388/etd-12232004-135619en_US
dc.description.abstractHidden Markov models are extensions of Markov models where each observation is the result of a stochastic process in one of several unobserved states. Though favored by many scientists because of its unique and applicable mathematical structure, its independence assumption between the consecutive observations hampered further application. Autoregressive hidden Markov model is a combination of autoregressive time series and hidden Markov chains. Observations are generated by a few autoregressive time series while the switches between each autoregressive time series are controlled by a hidden Markov chain. In this thesis, we present the basic concepts, theory and associated approaches and algorithms for hidden Markov models, time series and autoregressive hidden Markov models. We have also built a bivariate autoregressive hidden Markov model on the temperature data from the Pacific Ocean to understand the mechanism of El Nino. The parameters and the state path of the model are estimated through the Segmental K-mean algorithm and the state estimations of the autoregressive hidden Markov model have been compared with the estimations from a conventional hidden Markov model. Overall, the results confirm the strength of the autoregressive hidden Markov models in the El Nino study and the research sets an example of ARHMM's application in the meteorology.en_US
dc.language.isoen_USen_US
dc.subjectHMMen_US
dc.subjectARHMMen_US
dc.titleAutoregressive hidden Markov model with application in an El Niño studyen_US
thesis.degree.departmentMathematics and Statisticsen_US
thesis.degree.disciplineMathematics and Statisticsen_US
thesis.degree.grantorUniversity of Saskatchewanen_US
thesis.degree.levelMastersen_US
thesis.degree.nameMaster of Science (M.Sc.)en_US
dc.type.materialtexten_US
dc.type.genreThesisen_US
dc.contributor.committeeMemberMiket, Milivoj J.en_US
dc.contributor.committeeMemberKelly, Ivan W.en_US
dc.contributor.committeeMemberBremner, Murray R.en_US
dc.contributor.committeeMemberSrinivasan, Rajen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record