Confidence intervals for a binomial parameter based on binary data subject to false-positive misclassification

DOI10.1016/J.CSDA.2005.08.007zbMath1445.62052OpenAlexW2061964548MaRDI QIDQ959417

Doyle H. Boese, James D. Stamey, Dean M. Young

Publication date: 11 December 2008

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2005.08.007

zbMATH Keywords

confidence intervalbinary datamisclassificationdouble samplingfalse positivelikelihood statistic

Mathematics Subject Classification ID

Parametric tolerance and confidence regions (62F25)

Related Items (15)

Maximum likelihood estimation of a binomial proportion using one‐sample misclassified binary data ⋮ Bayesian adjustment for unidirectional misclassification in ordinal covariates ⋮ An integrated‐likelihood‐ratio confidence interval for a proportion based on underreported and infallible data ⋮ Divergence-based confidence intervals in false-positive misclassification model ⋮ Confidence interval construction for disease prevalence based on partial validation series ⋮ Likelihood-based confidence intervals for the risk ratio using double sampling with over-reported binary data ⋮ Survival Weibull regression model for mismeasured outcomes ⋮ Interval estimation for misclassification rate parameters in a complementary Poisson model ⋮ Credible sets for risk ratios in over-reported two-sample binomial data using the double-sampling scheme ⋮ One-way analysis of proportions for misclassified binomial data ⋮ A Bayesian approach to obtain confidence intervals for binomial proportion in a double sampling scheme subject to false-positive misclassification ⋮ Bayesian confidence intervals of proportion with misclassified binary data ⋮ Confidence intervals for parameters of two diagnostic tests in the absence of a gold standard ⋮ Likelihood-Based Confidence Intervals for Complementary Poisson Rate Parameters with Misclassified Data ⋮ Interval-censored data with misclassification: a Bayesian approach

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