A Bayesian approach to obtain confidence intervals for binomial proportion in a double sampling scheme subject to false-positive misclassification

The construction of a confidence interval based on a Bayesian approach is considered for the population proportion with double sampled data subject to false-positive misclassification. The Bayesian confidence intervals are compared with a standard frequentist confidence interval in terms of coverage...

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Published in:Journal of the Korean Statistical Society 2008, 37(4), , pp.393-403
Main Authors: Lee, Seung-Chun, Byun, Jong-Seok
Format: Article
Language:English
Subjects:
62F15
62F25
Agresti–Coull type interval
Applied Statistics
Bayesian Inference
Coverage probability
Double sampling
Expected width
False-positive misclassification
Noninformative Bayes approach
primary
secondary
Statistical Theory and Methods
Statistics
Statistics and Computing/Statistics Programs
통계학
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container_title Journal of the Korean Statistical Society
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description The construction of a confidence interval based on a Bayesian approach is considered for the population proportion with double sampled data subject to false-positive misclassification. The Bayesian confidence intervals are compared with a standard frequentist confidence interval in terms of coverage probability and expected width. It is shown that a noninformative Bayes approach provides a relatively simple but effective confidence interval.
doi_str_mv 10.1016/j.jkss.2008.05.001
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ispartof Journal of the Korean Statistical Society, 2008, 37(4), , pp.393-403
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source Springer Nature
subjects 62F15
62F25
Agresti–Coull type interval
Applied Statistics
Bayesian Inference
Coverage probability
Double sampling
Expected width
False-positive misclassification
Noninformative Bayes approach
primary
secondary
Statistical Theory and Methods
Statistics
Statistics and Computing/Statistics Programs
통계학
title A Bayesian approach to obtain confidence intervals for binomial proportion in a double sampling scheme subject to false-positive misclassification
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