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

Pashkevich, Maxim A., and Kharin, Yurij S.. "Robust estimation and forecasting for beta-mixed hierarchical models of grouped binary data.." SORT 28.2 (2004): 125-160. <http://eudml.org/doc/40460>.

@article{Pashkevich2004,
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 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.},
author = {Pashkevich, Maxim A., Kharin, Yurij S.},
journal = {SORT},
keywords = {Inferencia paramétrica; Estimadores robustos; Modelo jerárquico; grouped binary data; distortions; hierarchical models; beta-binomial; beta-logistic; robust; estimation; forecasting},
language = {eng},
number = {2},
pages = {125-160},
title = {Robust estimation and forecasting for beta-mixed hierarchical models of grouped binary data.},
url = {http://eudml.org/doc/40460},
volume = {28},
year = {2004},
}

TY - JOUR
AU - Pashkevich, Maxim A.
AU - Kharin, Yurij S.
TI - Robust estimation and forecasting for beta-mixed hierarchical models of grouped binary data.
JO - SORT
PY - 2004
VL - 28
IS - 2
SP - 125
EP - 160
AB - 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.
LA - eng
KW - Inferencia paramétrica; Estimadores robustos; Modelo jerárquico; grouped binary data; distortions; hierarchical models; beta-binomial; beta-logistic; robust; estimation; forecasting
UR - http://eudml.org/doc/40460
ER -