Permuting density-based empirical likelihood ratio test statistics

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.