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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2017.11
- 수록면
- 1,547 - 1,555 (9page)
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This paper studies Bayesian test of homogeneity in contingency tables made by discretizing a continuous variable. Sometimes when we are considering events of interest in small area setup, we can think of discretization approaches about the continuous variable. If we properly discretize the continuous variable, we can nd invisible relationships between areas (groups) and a continuous variable of interest. The proper discretization of the continuous variable can support the alternative hypothesis of the homogeneity test in contingency tables even if the null hypothesis was not rejected through k-sample tests involving one-way ANOVA. In other words, the proportions of variables with a particular level can vary from group to group by the discretization. If we discretize the the continuous variable, it can be treated as an analysis of the contingency table. In this case, the chi-squared test is the most commonly employed method. However, further discretization gives rise to more cells in the table. As a result, the count in the cells becomes smaller and the accuracy of the test becomes lower. To prevent this, we can consider the Bayesian approach and apply it to the setup of the homogeneity test.
목차
Abstract
1. Introduction
2. Bayesian hierarchical model for test of homegenity
3. Numerical study
4. Concluding remarks
References
참고문헌 (11)
참고문헌 신청
Agresti, A. and Hitchcock, D. B. (2005). Bayesian inference for categorical data analysis. Statistical Methods and Applications, 14, 297-330.
Cressie, N. and Read, T. R. C. (1984). Multinomial goodness-of-fit tests. Journal of the Royal Statistical Society, Series B, 46, 440-464.
Jiang, B., Ye, C., and Liu, J. S. (2015). Nonparametric K-sample tests via dynamic slicing. Journal of the American Statistical Association, 110, 642-653.
Jo, A., and Kim, D. H. (2017). Bayes tests of independence for contingency tables from small areas. Journal of the Korean Data & Information Science Society, 28, 207-215.
Kass, R. E. and Raftery, A. E. (1995). Bayes factor. Journal of the American Statistical Association, 90, 773-795.
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