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dc.contributor.authorPashkevich, Maxim A.
dc.contributor.authorKharin, Yurij S.
dc.date.accessioned2007-11-12T19:25:15Z
dc.date.available2007-11-12T19:25:15Z
dc.date.issued2004
dc.identifier.citationPashkevich, Maxim A.; Kharin, Yurij S.. "Robust estimation and forecasting for beta-mixed hierarchical models of grouped binary data". SORT, 2004, Vol. 28, núm. 2
dc.identifier.issn1696-2281
dc.identifier.urihttp://hdl.handle.net/2099/3753
dc.description.abstractThe paper focuses on robust estimation and forecasting techniques for grouped binary data with misclassified responses. It is assumed that the data are described by the beta-mixed hierarchical model (the beta-binomial or the beta-logistic), while the misclassifications are caused by the stochastic additive distortions of binary observations. For these models, the effect of ignoring the misclassifications is evaluated and expressions for the biases of the method-of-moments estimators and maximum likelihood estimators, as well as expressions for the increase in the mean square error of forecasting for the Bayes predictor are given. To compensate the misclassification effects, new consistent estimators and a new Bayes predictor, which take into account the distortion model, are constructed. The robustness of the developed techniques is demonstrated via computer simulations and a real-life case study.
dc.format.extent125-160
dc.language.isoeng
dc.publisherInstitut d'Estadística de Catalunya
dc.relation.ispartofSORT. 2004, Vol. 28, Núm. 2 [July-December]
dc.rightsAttribution-NonCommercial-NoDerivs 2.5 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/2.5/es/
dc.subject.otherInference
dc.titleRobust estimation and forecasting for beta-mixed hierarchical models of grouped binary data
dc.typeArticle
dc.subject.lemacInferència
dc.description.peerreviewedPeer Reviewed
dc.subject.amsClassificació AMS::62 Statistics::62F Parametric inference
dc.rights.accessOpen Access
local.personalitzacitaciotrue


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