Browse > Article
http://dx.doi.org/10.5351/KJAS.2013.26.6.943

On Prediction Intervals for Binomial Data  

Ryu, Jea-Bok (Department of Statistics, College of Science & Engineering, Cheongju University)
Publication Information
The Korean Journal of Applied Statistics / v.26, no.6, 2013 , pp. 943-952 More about this Journal
Abstract
Wald, Agresti-Coull, Jeffreys, and Bayes-Laplace methods are commonly used for confidence interval of binomial proportion are applied for prediction intervals. We used coverage probability, mean coverage probability, root mean squared error, and mean expected width for numerical comparisons. From the comparisons, we found that Wald is not proper as for confidence interval and Agresti-Coull is too conservative to differ from confidence interval. However, Jeffrey and Bayes-Laplace are good for prediction interval and Jeffrey is especially desirable as for confidence interval.
Keywords
Binary data; prediction interval; coverage probability; mean coverage probability; root mean squared error; mean expected width;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 Agresti, A. and Coull, B. A. (1998). Approximate is better than "Exact" for interval estimation of Binomial proportions, The American Statistician, 52, 119-126.
2 Brown, L. D., Cai, T. T. and DasGupta, A. (2001). Interval estimation for a binomial proportion(with discussion), Statistical Science, 16, 101-133.
3 Brown, L. D., Cai, T. T. and DasGupta, A. (2002). Confidence intervals for a binomial proportion and asymptotic expansions, The Annals of Statistics, 30, 160-201.   DOI   ScienceOn
4 Geisser, S. (1984). On prior distribution for binary trials(with discussion), The American Statistician, 38, 244-247.
5 Hahn, G. J. and Meeker, W. Q. (1991). Statistical Intervals - A Guide for Practitioners, John Wiley & Sons, Inc.
6 Hall, P. and Rieck, A. (2001). Improving coverage accuracy of nonparametric prediction intervals, Journal of the Royal Statistical Society, Series B, 63, 717-725.   DOI   ScienceOn
7 Nelson, W. B. (2004). Applied Life Data Analysis, John Wiley & Sons, Inc.
8 Ryu, J. B. (2009). A short consideration of binomial confidence interval, Communications of the Korean Statistical Society, 16, 731-743.   DOI   ScienceOn
9 Ryu, J. B. (2010). The effect of adjusting the extreme values inWald confidence interval, Journal of Research Institute of Industrial Sciences, 28, 29-34.
10 Ryu, J. B. (2011). The influence of extreme value in binomial confidence interval, Communications of the Korean Statistical Society, 18, 615-623.   DOI   ScienceOn
11 Ryu, J. B. and Lee, S. J. (2006). Confidence intervals for a low binomial proportion, The Korean Journal of Applied Statistics, 19, 217-230.   DOI   ScienceOn
12 Tuyl, F., Gerlach, R. and Mengersen, K. (2008). A comparison of Bayes-Laplace, Jeffreys, and other priors: The case of zero events, The American Statistician, 62, 40-44.   DOI   ScienceOn
13 Tuyl, F., Gerlach, R. and Mengersen, K. (2009). Posterior predictive arguments in favor of the Bayes-Laplace prior as the consensus priors for binomial and multinomial parameters, Bayesian Analysis, 4, 151-158.   DOI
14 Vidoni, P. (2009). Improving prediction intervals and distributional functions, Scandinavian Journal of Statistics, 36, 735-748.   DOI   ScienceOn
15 Wang, H. (2008). Coverage probability of prediction intervals for discrete random variable, Computational Statistics and Data Analysis, 53, 17-26.   DOI   ScienceOn
16 Yu, K. and Ally, A. (2009). Improving prediction intervals: Some elementary methods, The American Statistician, 63, 17-19.   DOI   ScienceOn
17 Winkler, R. L., Smith, J. E. and Fryback, D. G. (2002). The role of informative priors in zero-numerator problems: Being conservative versus being candid, The American Statistician, 56, 1-4.   DOI   ScienceOn