Document Type
Article
Publication Date
4-6-2019
Abstract
The classical unconditional exact p-value test can be used to compare two multinomial distributions with small samples. This general hypothesis requires parameter estimation under the null which makes the test severely conservative. Similar property has been observed for Fisher's exact test with Barnard and Boschloo providing distinct adjustments that produce more powerful testing approaches. In this study, we develop a novel adjustment for the conservativeness of the unconditional multinomial exact p-value test that produces nominal type I error rate and increased power in comparison to all alternative approaches. We used a large simulation study to empirically estimate the 5th percentiles of the distributions of the p-values of the exact test over a range of scenarios and implemented a regression model to predict the values for two-sample multinomial settings. Our results show that the new test is uniformly more powerful than Fisher's, Barnard's, and Boschloo's tests with gains in power as large as several hundred percent in certain scenarios. Lastly, we provide a real-life data example where the unadjusted unconditional exact test wrongly fails to reject the null hypothesis and the corrected unconditional exact test rejects the null appropriately.
Recommended Citation
Louis Ehwerhemuepha, Heng Sok & Cyril Rakovski (2019): A more powerful unconditional exact test of homogeneity for 2 × c contingency table analysis, Journal of Applied Statistics, DOI: 10.1080/02664763.2019.1601689
Peer Reviewed
1
Copyright
The authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Included in
Applied Statistics Commons, Other Applied Mathematics Commons, Other Mathematics Commons
Comments
This article was originally published in Journal of Applied Statistics in 2019. DOI: 10.1080/02664763.2019.1601689