Abstract
In this article we study two permutation tests based on the density-based empirical likelihood ratio (DBELR) test statistics with application to paired and two-sample data. The DBELR tests have been extensively studied in statistical literature and are widely recognized for their distribution-free nature and high, stable power across various testing problems. The proposed methods enhance the use of DBELR tests in a more convenient manner that significantly reduces computation times and generates permutation p-values to make test decisions while maintaining the desired level α. An extensive Monte Carlo simulation study shows that the proposed methods inherit the stable and high-power property of the original DBELR tests. A real-world data example is used to demonstrate the applicability of the proposed methods.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Acknowledgements
The authors thank two reviewers for their insightful and helpful comments that led to a substantial improvement in this article.