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Confidence intervals for the ratio of two Poisson rates under one-way differential misclassification using double sampling

Author

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  • Kahle, David J.
  • Young, Phil D.
  • Greer, Brandi A.
  • Young, Dean M.

Abstract

Wald, profile likelihood, and marginal likelihood confidence intervals are derived for the ratio of two Poisson rates in the presence of one-way differentially misclassified data using double sampling. Monte Carlo simulations demonstrate the reliability and relative performance of the intervals, and an example from cancer epidemiology illustrates their application and interpretation in a real-world scenario. All of the methods described are implemented and freely available in the R package poisDoubleSamp on the Comprehensive R Archive Network (CRAN).

Suggested Citation

  • Kahle, David J. & Young, Phil D. & Greer, Brandi A. & Young, Dean M., 2016. "Confidence intervals for the ratio of two Poisson rates under one-way differential misclassification using double sampling," Computational Statistics & Data Analysis, Elsevier, vol. 95(C), pages 122-132.
    • Handle: RePEc:eee:csdana:v:95:y:2016:i:c:p:122-132
    DOI: 10.1016/j.csda.2015.09.013
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    1. Micha Mandel, 2013. "Simulation-Based Confidence Intervals for Functions With Complicated Derivatives," The American Statistician, Taylor & Francis Journals, vol. 67(2), pages 76-81, May.
    2. Thomas A. Severini, 2007. "Integrated likelihood functions for non-Bayesian inference," Biometrika, Biometrika Trust, vol. 94(3), pages 529-542.
    3. B. K. Singh & S. Choudhury, 2012. "A class of chain ratio-cum-dual to ratio type estimator with two auxiliary characters under double sampling in sample surveys," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 13(3), pages 519-536, December.
    4. A. S. Whittemore & G. Gong, 1991. "Poisson Regression with Misclassified Counts: Application to Cervical Cancer Mortality Rates," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 40(1), pages 81-93, March.
    5. Eddelbuettel, Dirk & Francois, Romain, 2011. "Rcpp: Seamless R and C++ Integration," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 40(i08).
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