ESTIMAÇÃO ROBUSTA DOS PARÂMETROS DA DISTRIBUIÇÃO BETA
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Resumo
This paper proposes robust estimators for parameters of the beta distribution, useful for modelling continuous data restricted to the interval (0,1) in the presence of outliers. Weighted maximum likelihood estimators (WMLE) with two weighting proposals and a minimum distance estimator (MDE) were considered. The bias correction of these estimators was performed using the parametric bootstrap method adapted for data with outliers. The performances of the proposed estimators were evaluated by means of Monte Carlo simulations and their results compared to the performance of the usual estimators by the moment and maximum likelihood methods. The breakdown point and the sensitivity curve were adopted as specic measures of robustness. The numerical evaluation pointed out that the WMLE and MDE have lower sensitivity to outliers and greater breakdown points. The estimators corrected by the proposed bootstrap method had lower bias compared to their versions uncorrected. Moreover, an application to real data is also presented and discussed.
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