Improved Empirical Bayes Estimation in Group Testing Procedure for Small Proportions |
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Authors: | Xiang Fang Walter W. Stroup Shunpu Zhang |
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Affiliation: | 1. Department of Statistics , University of Nebraska-Lincoln , Lincoln, Nebraska, USA fangx@bigred.unl.edu;3. Department of Statistics , University of Nebraska-Lincoln , Lincoln, Nebraska, USA |
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Abstract: | Group testing has been long recognized as an efficient method to classify all the experimental units into two mutually exclusive categories: defective or not defective. In recent years, more attention has been brought to the estimation of the population prevalence rate p of a disease, or of some property, using group testing. In this article, we propose two scaled squared-error loss functions, which improve the Bayesian approach to estimating p in terms of minimizing the mean squared error (MSE) of the Bayes estimators of p for small p. We show that the new estimators are preferred over the estimator from the usual squared-error loss function and the maximum likelihood estimator (MLE) for small p. |
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Keywords: | Emperical Bayes estimation Group testing Loss function Proportion |
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