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Confidence interval estimation under inverse sampling

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  • Zou, G.Y.

Abstract

In comparative studies of rare events, fixing group sizes may result in groups with zero events. To overcome this difficulty, one may adopt an inverse sampling design which fixes the number of events, resulting in random variables following the negative binomial distribution. This article presents a new approach to setting confidence intervals for effect measures under inverse sampling, using the variance estimates recovered from exact confidence limits for single negative binomial proportions. Exact numerical evaluation results demonstrate that the proposed procedure performs well.

Suggested Citation

  • Zou, G.Y., 2010. "Confidence interval estimation under inverse sampling," Computational Statistics & Data Analysis, Elsevier, vol. 54(1), pages 55-64, January.
    • Handle: RePEc:eee:csdana:v:54:y:2010:i:1:p:55-64
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    References listed on IDEAS

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    1. Zou, Guang Yong & Huang, Wenyi & Zhang, Xiaohe, 2009. "A note on confidence interval estimation for a linear function of binomial proportions," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 1080-1085, February.
    2. Tang, Man-Lai & Tian, Maozai, 2009. "Asymptotic confidence interval construction for risk difference under inverse sampling," Computational Statistics & Data Analysis, Elsevier, vol. 53(3), pages 621-631, January.
    3. Agresti, Alan & Gottard, Anna, 2007. "Nonconservative exact small-sample inference for discrete data," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 6447-6458, August.
    4. Zou, Guang Yong & Taleban, Julia & Huo, Cindy Y., 2009. "Confidence interval estimation for lognormal data with application to health economics," Computational Statistics & Data Analysis, Elsevier, vol. 53(11), pages 3755-3764, September.
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    Cited by:

    1. Mathew, Thomas & Young, Derek S., 2013. "Fiducial-based tolerance intervals for some discrete distributions," Computational Statistics & Data Analysis, Elsevier, vol. 61(C), pages 38-49.
    2. Niu, Cuizhen & Guo, Xu & McAleer, Michael & Wong, Wing-Keung, 2018. "Theory and application of an economic performance measure of risk," International Review of Economics & Finance, Elsevier, vol. 56(C), pages 383-396.

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