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dc.date.accessioned2013-11-21T11:55:56Z
dc.date.available2013-11-21T11:55:56Z
dc.date.issued1999en_US
dc.date.submitted2012-01-19en_US
dc.identifier.urihttp://hdl.handle.net/10852/10422
dc.description.abstractSuppose that a sequence of data points follows a distribution of a certain parametric form, but that one or more of the underlying parameters may change over time. This paper addresses various natural questions in such a framework. We construct canonical monitoring processes which under the hypothesis of no change converge in distribution to independent Brownian bridges, and use these to construct natural goodness-of-fit statistics. Weighted versions of these are also studied, and optimal weight functions are derived to give maximum local power against alternatives of interest. We also discuss how our results can be used to pinpoint where and what type of changes have occurred, in the event that initial screening tetsts indicate that such exist. Our unified large- sample methodology is quite general and applies to all regular parametric models including regression, Markov chain and time series situations.eng
dc.language.isoengen_US
dc.publisherMatematisk Institutt, Universitetet i Oslo
dc.relation.ispartofPreprint series. Statistical Research Report http://urn.nb.no/URN:NBN:no-23420en_US
dc.relation.urihttp://urn.nb.no/URN:NBN:no-23420
dc.titleTests for Constancy of Model Paratneters Over Timeen_US
dc.typeResearch reporten_US
dc.date.updated2013-11-15en_US
dc.creator.authorHjort, Nils Liden_US
dc.subject.nsiVDP::410en_US
dc.identifier.urnURN:NBN:no-30245en_US
dc.type.documentForskningsrapporten_US
dc.identifier.duo149335en_US
dc.identifier.fulltextFulltext https://www.duo.uio.no/bitstream/handle/10852/10422/1/03-99.pdf


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