Prediction and Classification of Non-stationary Categorical Time Series

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dc.contributor.authorFokianos, Konstantinosen_US
dc.contributor.authorKedem, Benjaminen_US
dc.contributor.departmentISRen_US
dc.date.accessioned2007-05-23T10:01:51Z
dc.date.available2007-05-23T10:01:51Z
dc.date.issued1996en_US
dc.description.abstractPartial Likelihood analysis of a general regression model for the analysis of non-stationary categorical time series is presented, taking into account stochastic time dependent covariates. The model links the probabilities of each category to a covariate process through a vector of time invariant parameters. Under mild regularity conditions, we establish good asymptotic properties of the estimator by appealing to martingale theory. Certain diagnostic tools are presented for checking the adequacy of the fit.en_US
dc.format.extent683339 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/1903/5768
dc.language.isoen_USen_US
dc.relation.ispartofseriesISR; TR 1996-49en_US
dc.subjectmathematical modelingen_US
dc.subjectestimationen_US
dc.subjectNon-stationarity classificationen_US
dc.subjectpredictionen_US
dc.subjectasymptotic theoryen_US
dc.subjectpartial likelihooden_US
dc.subjectgoodness of fit,en_US
dc.titlePrediction and Classification of Non-stationary Categorical Time Seriesen_US
dc.typeTechnical Reporten_US

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