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In this study we present a two-stage adaptive estimator of prevalence in the presence of test errors. We assume that tests are not 100% perfect. We obtain the adaptive estimator using Maximum Likelihood Estimate (MLE) method and use Fisher information to determine the variance of the estimator. We use Matlab, a statistical software for simulation and verification of the model. We analyse and discuss the properties of the constructed estimator in comparison with other existing estimators in the literature of pool testing. We also provide the confidence interval of the estimator. When the test…mehr

Produktbeschreibung
In this study we present a two-stage adaptive estimator of prevalence in the presence of test errors. We assume that tests are not 100% perfect. We obtain the adaptive estimator using Maximum Likelihood Estimate (MLE) method and use Fisher information to determine the variance of the estimator. We use Matlab, a statistical software for simulation and verification of the model. We analyse and discuss the properties of the constructed estimator in comparison with other existing estimators in the literature of pool testing. We also provide the confidence interval of the estimator. When the test kits have low sensitivity and specificity, we establish that the adaptive estimator outperforms other existing estimators. Further more, we demonstrate that the efficiency of the adaptive estimation scheme improves as the number of stages increases. This makes the adaptive testing scheme more ideal in areas where errors are rampant.
Autorenporträt
Annette Wakaanya Okoth holds a Master of Science Degree in Statistics of Masinde Muliro University and a B.Ed Science Degree of Egerton University in Mathematics and Chemistry. She is a distinguished lecturer of statistics at Kibabii Diploma TTC and Mt. Kenya University. Annette is currently developing her PhD concept paper in Statistics.