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13.349 Articles in journals published by the UPC
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Robust estimation and forecasting for beta-mixed hierarchical models of grouped binary data

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Pashkevich, Maxim A.
Kharin, Yurij S.
Document typeArticle
Defense date2004
PublisherInstitut d'Estadística de Catalunya
Rights accessOpen Access
Attribution-NonCommercial-NoDerivs 2.5 Spain
Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution-NonCommercial-NoDerivs 2.5 Spain
Abstract
The 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.
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 
URIhttp://hdl.handle.net/2099/3753
ISSN1696-2281
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  • SORT (Statistics and Operations Research Transactions) - 2004, Vol. 28, Núm. 2 [9]
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