Exact Confidence Intervals for Proportions Estimated by Group Testing

DOI10.2307/2533075zbMath0875.62545OpenAlexW2330975800MaRDI QIDQ4335789

Graham Hepworth

Publication date: 9 November 1997

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.2307/2533075

Mathematics Subject Classification ID

Parametric tolerance and confidence regions (62F25) Applications of statistics to biology and medical sciences; meta analysis (62P10) Sequential statistical analysis (62L10)

Related Items (20)

Estimating proportions by group retesting with unequal group sizes at each stage ⋮ Estimation of proportions by group testing with retesting of positive groups ⋮ Nonparametric estimation of distributions and diagnostic accuracy based on group‐tested results with differential misclassification ⋮ Particle count estimation in dilution series experiments ⋮ Bias correction in estimating proportions by pooled testing ⋮ Improved estimation of proportions using inverse binomial group testing ⋮ Sequential estimation in the group testing problem ⋮ Confidence interval procedures for proportions estimated by group testing with groups of unequal size adjusted for overdispersion ⋮ Properties of a One-Sided Likelihood Ratio Test as Applied to Pool Screening ⋮ Large-sample hypothesis tests for stratified group-testing data ⋮ Confidence intervals for the difference of two proportions estimated from pooled samples ⋮ Estimating disease prevalence using inverse binomial pooled testing ⋮ Estimation of the Proportion of Defective Units by Using Group Testing Under the Existence of a Threshold of Detection ⋮ A New Estimator for a Population Proportion Using Group Testing ⋮ Bayesian inference for disease prevalence using negative binomial group testing ⋮ Pool Screening: An Example of Independent Nonidentical Bernoulli Trial ⋮ Bias correction of estimated proportions using inverse binomial group testing ⋮ Improved exact confidence intervals for a proportion using ranked-set sampling ⋮ Evaluation of a Frequentist Hierarchical Model to Estimate Prevalence When Sampling from a Large Geographic Area Using Pool Screening ⋮ Robust procedures for experimental design in group testing considering misclassification

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