The SETAR Model of Tong and Lim and Advances in Computation

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dc.contributor.author Geweke, John en_US
dc.contributor.editor Chan, KS en_US
dc.date.accessioned 2010-05-28T09:37:44Z
dc.date.available 2010-05-28T09:37:44Z
dc.date.issued 2009 en_US
dc.identifier 2009000203 en_US
dc.identifier.citation Geweke John 2009, 'The SETAR Model of Tong and Lim and Advances in Computation', in NA (ed.), World Scientific Publishing Co., Singapore, pp. 85-94. en_US
dc.identifier.issn 978-981-283-627-4 en_US
dc.identifier.other B1 en_US
dc.identifier.uri http://hdl.handle.net/10453/7818
dc.description.abstract This discussion revisits Tong and Lim's seminal 1980 paper on the SETAR model in the context of advances in computation since that time. Using the Canadian lynx data set from that paper, it compares exact maximum likelihood estimates with those in the original paper. It illustrates the application of Bayesian MCMC methods, developed in the intervening years, to this model and data set. It shows that SETAR is a limiting case of mixture of experts models and studies the application of one variant of those models to the lynx data set. The application is successful, despite the small size of the data set and the complexity of the model. Predictive likelihood ratios favor Tong and Lim's original model. en_US
dc.language en_US
dc.publisher World Scientific en_US
dc.relation.isbasedon NA en_US
dc.title The SETAR Model of Tong and Lim and Advances in Computation en_US
dc.parent Exploration of a Nonlinear World: An Appreciation of Howell Tong's Contributions to Statistics. en_US
dc.journal.volume en_US
dc.journal.number en_US
dc.publocation Singapore en_US
dc.identifier.startpage 85 en_US
dc.identifier.endpage 94 en_US
dc.cauo.name BUS.Faculty of Business en_US
dc.conference Verified OK en_US
dc.for 010405 en_US
dc.personcode 101228 en_US
dc.percentage 100 en_US
dc.classification.name Statistical Theory en_US
dc.classification.type FOR-08 en_US
dc.edition 1 en_US
dc.custom en_US
dc.date.activity en_US
dc.location.activity en_US
dc.description.keywords NA en_US
dc.staffid 101228 en_US


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