Empirical Bayes methods in time series analysis

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dc.contributor.authorKhoshgoftaar, Taghi M.en
dc.contributor.committeechairKrutchkoff, Richard G.en
dc.contributor.committeememberHinkelmann, Klausen
dc.contributor.committeememberFoutz, Robert V.en
dc.contributor.committeememberMyers, Raymonden
dc.contributor.committeememberMann, Jerry E.en
dc.contributor.departmentStatisticsen
dc.date.accessioned2019-03-26T19:53:32Zen
dc.date.available2019-03-26T19:53:32Zen
dc.date.issued1982en
dc.description.abstractIn the case of repetitive experiments of a similar type, where the parameters vary randomly from experiment to experiment, the Empirical Bayes method often leads to estimators which have smaller mean squared errors than the classical estimators. Suppose there is an unobservable random variable θ, where θ ~ G(θ), usually called a prior distribution. The Bayes estimator of θ cannot be obtained in general unless G(θ) is known. In the empirical Bayes method we do not assume that G(θ) is known, but the sequence of past estimates is used to estimate θ. This dissertation involves the empirical Bayes estimates of various time series parameters: The autoregressive model, moving average model, mixed autoregressive-moving average, regression with time series errors, regression with unobservable variables, serial correlation, multiple time series and spectral density function. In each case, empirical Bayes estimators are obtained using the asymptotic distributions of the usual estimators. By Monte Carlo simulation the empirical Bayes estimator of first order autoregressive parameter, ρ, was shown to have smaller mean squared errors than the conditional maximum likelihood estimator for 11 past experiences.en
dc.description.degreeDoctor of Philosophyen
dc.format.extentvi, 100, [1] leavesen
dc.format.mimetypeapplication/pdfen
dc.identifier.urihttp://hdl.handle.net/10919/88723en
dc.language.isoen_USen
dc.publisherVirginia Polytechnic Institute and State Universityen
dc.relation.isformatofOCLC# 8908468en
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subject.lccLD5655.V856 1982.K538en
dc.subject.lcshTime-series analysisen
dc.subject.lcshBayesian statistical decision theoryen
dc.titleEmpirical Bayes methods in time series analysisen
dc.typeDissertationen
dc.type.dcmitypeTexten
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.leveldoctoralen
thesis.degree.nameDoctor of Philosophyen

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