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

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  • Dewi Rahardja

    (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.)

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.

Suggested Citation

  • Dewi Rahardja, 2019. "Bayesian Inference for the Difference of Two Proportion Parameters in Over-Reported Two-Sample Binomial Data Using the Doubly Sample," Stats, MDPI, vol. 2(1), pages 1-10, February.
    • Handle: RePEc:gam:jstats:v:2:y:2019:i:1:p:9-120:d:204873
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    References listed on IDEAS

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    1. Gordon J. Prescott & Paul H. Garthwaite, 2002. "A Simple Bayesian Analysis of Misclassified Binary Data with a Validation Substudy," Biometrics, The International Biometric Society, vol. 58(2), pages 454-458, June.
    2. Mary J. Morrissey & Donna Spiegelman, 1999. "Matrix Methods for Estimating Odds Ratios with Misclassified Exposure Data: Extensions and Comparisons," Biometrics, The International Biometric Society, vol. 55(2), pages 338-344, June.
    3. Boese, Doyle H. & Young, Dean M. & Stamey, James D., 2006. "Confidence intervals for a binomial parameter based on binary data subject to false-positive misclassification," Computational Statistics & Data Analysis, Elsevier, vol. 50(12), pages 3369-3385, August.
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    Cited by:

    1. Dewi Rahardja, 2022. "Omnibus Tests for Multiple Binomial Proportions via Doubly Sampled Framework with Under-Reported Data," Stats, MDPI, vol. 5(2), pages 1-14, April.

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