References
- Agresti, A. and Caffo, B. (2000). Simple and effective confidence intervals for proportions and differences of proportions result from adding two successes and two failures, The American Statistician, 54, 280–288.
- Agresti, A. and Coull, B. A. (1998). Approximation is better than “exact” for interval estimation of binomial proportions, The American Statistician, 52, 119–126.
- Barndorff-Nielsen, O. E. and Cox, D. R. (1994). Inference and Asymptotics, Chapman & Hall, London.
- Barnett, V., Haworth, J. and Smith, T. M. F. (2001). A two-phase sampling scheme with applications to auditing or sed quis custodiet ipsos custodes?, Journal of Royal Statistical Society, Serie A, 164, 407–422. https://doi.org/10.1111/1467...985X.00210
- Boese, D. H., Young, D. M. and Stamey, J. D. (2006). Confidence intervals for a binomial parameter based on binary data subject to false-positive misclassification, Computational Statistics and Data Analysis, 50, 3369–3385. https://doi.org/10.1016/j.csda.2005.08.007
- Brown, L. D., Cai, T. T. and DasGupta, A. (2001). Interval estimation for a binomial proportion, Statistical Science, 16, 101–133. https://doi.org/10.1214/ss/1009213286
- Efron, B. and Hinkley, D. V. (1978). Assessing the accuracy of the maximum likelihood estimator: Observed versus expected Fisher information, Biometrika, 65, 457–482. https://doi.org/10.1093/biomet/65.3.457
- Geng, Z. and Asano, C. (1989). Bayesian estimation methods for categorical data with misclassifications, Communications in Statistics, Theory and Methods, 18, 2935–2954. https://doi.org/10.1080/03610928908830069
- Hildesheim, A., Mann, V., Brinton, L. A., Szklo, M., Reeves, W. C. and Rawls, W. E. (1991). Herpes simplex virus type 2: A possible interaction with human papillomavirus types 16/18 in the development of invasion cervical cancer, International Journal of Cancer, 49, 335–340. https://doi.org/10.1002/ijc.2910490304
- Lee, S. C. (2010). Confidence intervals for the difference of binomial proportions in two doubly sampled data, Communications of the Korean Statistical Association, 3, 301–310.
- Lee, S. C. and Byun, J. S. (2008). A Bayesian approach to obtain confidence intervals for binomial proportion in a double sampling scheme subject to false-positive misclassification, Journal of the Korean Statistical Society, 37, 393–403. https://doi.org/10.1016/j.jkss.2008.05.001
- Lie, R. T., Heuch, I. and Irgens, L. M. (1994). Maximum likelihood estimation of proportion of congenital malformations using double registration systems, Biometrics, 50, 433–444.
- Moors, J. J. A., van der Genugten, B. B. and Strijbosch, L. W. G. (2000). Repeated audit controls, Statistica Neerlandica, 54, 3–13. https://doi.org/10.1111/1467-9574.00122
- Raats, V. M. and Moors, J. J. A. (2003). Double-checking auditors: A Bayesian approach, The Statistician, 52, 351–365. https://doi.org/10.1111/1467-9884.00364
- Tenenbein, A. (1970). A double sampling scheme for estimating from binomial data with misclassifications, Journal of the American Statistical Association, 65, 1350–1361.
- York, J., Madigan, D., Heuch, I. and Lie, R. T. (1995). Birth defects registered by double sampling: A Bayesian approach incorporating covariates and model uncertainty, Applied Statistics, 44, 227–242.
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