Robust estimation and forecasting for beta-mixed hierarchical models of grouped binary data.

Título inglés	Robust estimation and forecasting for beta-mixed hierarchical models of grouped binary data.
Título español	Estimación robusta y pronóstico para modelos jerárquicos beta-mezclados de datos binarios agrupados.
Autor/es	Pashkevich, Maxim A. ; Kharin, Yurij S.
Organización	Dep. Math. Model. & Data Anal. Fac. Appl. Math. Comput. Sci. Belarusian State Univ., Minsk, Bielorrusia
Revista	1696-2281
Publicación	2004, 28 (2): 125-160, 33 Ref.
Tipo de documento	articulo
Idioma	Inglés
Resumen inglés	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 distorsions 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 demostrated via computer simulations and a real-life case study.
Clasificación UNESCO	120913
Palabras clave español	Inferencia paramétrica ; Estimadores robustos ; Modelo jerárquico
Código MathReviews	MR2116188
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