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The Role of Artificial Observations in Misclassified Binary Data with Common False-Positive Error
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 Title & Authors
The Role of Artificial Observations in Misclassified Binary Data with Common False-Positive Error
Lee, Seung-Chun;
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 Abstract
An Agresti-Coull type test is considered for the difference of binomial proportions in two doubly sampled data subject to common false-positive error. The performance of the test is compared with likelihood-based tests. The Agresti-Coull test has many desirable properties in that it can approximate the nominal significance level well, and has comparable power performance with a computational advantage.
 Keywords
Agresti-Coull test;likelihood-based tests;profile likelihood;double sampling;
 Language
English
 Cited by
 References
1.
Agresti, A. and Coull, B. A. (1998). Approximation is better than "exact" for interval estimation of binomial proportions, The American Statistician, 52, 119-126.

2.
Barndorff-Nielsen, O. E. and Cox, D. R. (1994). Inference and Asymptotics, Chapman and Hall, London.

3.
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, Series A, 164, 407-422. crossref(new window)

4.
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. crossref(new window)

5.
Brown, L. D., Cai, T. T. and DasGupta, A. (2001). Interval estimation for a binomial proportion, Statistical Science, 16, 101-133.

6.
Efron, B. and Hinkley, D. V. (1978). Assessing the accuracy of the maximum likelihood estimator: Observed versus expected Fisher information, Biometrika, 65, 457-482. crossref(new window)

7.
Geng, Z. and Asano, C. (1989). Bayesian estimation methods for categorical data with misclassifications, Communications in Statistics, Theory and Methods, 18, 2935-2954. crossref(new window)

8.
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. crossref(new window)

9.
Lee, S.-C. (2006a). Interval estimation of binomial proportions based on weighted Polya posterior, Computational Statistics & Data Analysis, 51, 1012-1021. crossref(new window)

10.
Lee, S.-C. (2006b). The weighted Polya posterior confidence interval for the difference between two independent proportions, The Korean Journal of Applied Statistics, 19, 171-181. crossref(new window)

11.
Lee, S.-C. (2010). Likelihood based confidence intervals for the difference of proportions in two doubly sampled data with a common false-positive error rate, Communications of the Korean Statistical Society, 17, 679-688. crossref(new window)

12.
Lee, S.-C. (2012). The role of artificial observations in testing for the difference of proportions in misclassified binary data, The Korean Journal of Applied Statistics, 25, 513-520. crossref(new window)

13.
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. crossref(new window)

14.
Moors, J. J. A., van der Genugten, B. B. and Strijbosch, L. W. G. (2000). Repeated audit controls, Statistica Neerlandica, 54, 3-13. crossref(new window)

15.
Raats, V. M. and Moors, J. J. A. (2003). Double-checking auditors: A Bayesian approach, The Statistician, 52, 351-365.

16.
Tenenbein, A. (1970). A double sampling scheme for estimating from binomial data with misclassifications, Journal of the American Statistical Association, 65, 1350-1361. crossref(new window)

17.
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. crossref(new window)