Bayesian confidence intervals of proportion with misclassified binary data

Lee, Seung Chun

doi:10.1016/j.jkss.2012.09.001

Cited by 9 publications

(10 citation statements)

References 23 publications

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“…which has the same functional form as the sampling distribution in Equation (7). In general, it is nontrivial to sample from the posterior distribution (9). Therefore, we derive a closed-form algorithm for sampling from Equation (9) via the reparameterization of η.…”

Section: Modelmentioning

confidence: 99%

“…Raats and Moors [7] and Lee and Byun [8] reported Bayesian credible intervals for the true proportion parameter with one type of misclassification only. For one-sample data with both types of misclassification errors, Raats and Moors [7] derived both an exact confidence interval and a Bayesian credible interval for the true proportion parameter, and Lee [9] reported a Bayesian interval estimation for the true binomial proportion parameter. For two-sample data with two types of misclassification errors, Prescott and Garthwaite [10] proposed a Bayesian credible interval, and Morrissey and Spiegelman [11] derived likelihood-based confidence intervals for the odds ratio.…”

Section: Introductionmentioning

confidence: 99%

“…The uniform (0, 1) prior distribution in Equation 8is identical to a Beta (1, 1) distribution; however, to generalize this prior distribution in Expression (8) to be Beta (α, β) would complicate Equations (8) and (9). This would affect the generalized sensibility of the results in relation to the choice of the hyper-parameter values.…”

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confidence: 99%

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Bayesian Inference for the Difference of Two Proportion Parameters in Over-Reported Two-Sample Binomial Data Using the Doubly Sample

Rahardja

2019

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We construct a point and interval estimation using a Bayesian approach for the difference of two population proportion parameters based on two independent samples of binomial data subject to one type of misclassification. Specifically, we derive an easy-to-implement closed-form algorithm for drawing from the posterior distributions. For illustration, we applied our algorithm to a real data example. Finally, we conduct simulation studies to demonstrate the efficiency of our algorithm for Bayesian inference.

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Section: Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Bayesian Inference for the Difference of Two Proportion Parameters in Over-Reported Two-Sample Binomial Data Using the Doubly Sample

Rahardja

2019

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“…7 Lee and Byun 8 considered the CI construction for the binomial parameter based on a Bayesian approach. Lee 9 also considered Bayesian credible intervals for the binomial proportion subject to both false-positive and false-negative misclassifications. When the disease is rare or the case ascertainment procedure is difficult and/or costly, Morvan et al.…”

Section: Introductionmentioning

confidence: 99%

Test procedure and sample size determination for a proportion study using a double-sampling scheme with two fallible classifiers

Qiu

Zeng

Tang

et al. 2017

Stat Methods Med Res

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Double sampling is usually applied to collect necessary information for situations in which an infallible classifier is available for validating a subset of the sample that has already been classified by a fallible classifier. Inference procedures have previously been developed based on the partially validated data obtained by the double-sampling process. However, it could happen in practice that such infallible classifier or gold standard does not exist. In this article, we consider the case in which both classifiers are fallible and propose asymptotic and approximate unconditional test procedures based on six test statistics for a population proportion and five approximate sample size formulas based on the recommended test procedures under two models. Our results suggest that both asymptotic and approximate unconditional procedures based on the score statistic perform satisfactorily for small to large sample sizes and are highly recommended. When sample size is moderate or large, asymptotic procedures based on the Wald statistic with the variance being estimated under the null hypothesis, likelihood rate statistic, log- and logit-transformation statistics based on both models generally perform well and are hence recommended. The approximate unconditional procedures based on the log-transformation statistic under Model I, Wald statistic with the variance being estimated under the null hypothesis, log- and logit-transformation statistics under Model II are recommended when sample size is small. In general, sample size formulae based on the Wald statistic with the variance being estimated under the null hypothesis, likelihood rate statistic and score statistic are recommended in practical applications. The applicability of the proposed methods is illustrated by a real-data example.

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“…For one-sample data with both types of misclassification errors, Raats and Moors (2003) binomial proportion parameter, and Lee (2013) reported Bayesian interval estimation for the true binomial proportion parameter.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Credible Sets for Odds Ratio of Two Binomial Proportions Using Double Sampling with False-Positive Misclassification

Rahardja

2016

American Journal of Mathematical and Management Sciences

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KEY WORDS AND PHRASESBayesian approach; binary data; double sampling; false positive; interval estimation; misclassification; odds ratios SYNOPTIC ABSTRACTA Bayesian approach is considered for a point and interval estimation for odds ratios of two binomial proportion parameters using doublesampling data subject to false-positive misclassification. For such data, we derive an easy-to-implement, closed-form algorithm for drawing from the posterior distributions. Application of this algorithm is illustrated using data collected about student laboratory grades from a course.

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Bayesian confidence intervals of proportion with misclassified binary data

Cited by 9 publications

References 23 publications

Bayesian Inference for the Difference of Two Proportion Parameters in Over-Reported Two-Sample Binomial Data Using the Doubly Sample

Bayesian Inference for the Difference of Two Proportion Parameters in Over-Reported Two-Sample Binomial Data Using the Doubly Sample

Test procedure and sample size determination for a proportion study using a double-sampling scheme with two fallible classifiers

Bayesian Credible Sets for Odds Ratio of Two Binomial Proportions Using Double Sampling with False-Positive Misclassification

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