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

U.S. Department of Defense, Fort Meade, MD 20755, USA
Disclaimer Statement: This research represents the author’s own work and opinion. It does not reflect any policy nor represent the official position of the U.S. Department of Defense nor any other U.S. Federal Agency.
Stats 2019, 2(1), 111-120; https://doi.org/10.3390/stats2010009
Received: 9 December 2018 / Revised: 31 January 2019 / Accepted: 3 February 2019 / Published: 11 February 2019
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Abstract

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. View Full-Text
Keywords: Bayesian inference; binary data; double sampling; misclassification; two-sample; proportions difference Bayesian inference; binary data; double sampling; misclassification; two-sample; proportions difference
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Rahardja, D. Bayesian Inference for the Difference of Two Proportion Parameters in Over-Reported Two-Sample Binomial Data Using the Doubly Sample. Stats 2019, 2, 111-120.

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