Empirical phi-divergence test-statistics for the equalityof means of two populations

TL;DR

This paper extends empirical phi-divergence test-statistics to two-sample mean equality problems, demonstrating improved finite sample performance and competitiveness with classical tests through simulation studies.

Contribution

It introduces a novel application of empirical phi-divergence test-statistics for two-sample tests, enhancing finite sample behavior and robustness against model misspecification.

Findings

New test-statistics outperform classical tests in simulations

Empirical phi-divergence methods are effective for two-sample mean testing

Method shows robustness in finite samples

Abstract

Empirical phi-divergence test-statistics have demostrated to be a useful technique for the simple null hypothesis to improve the finite sample behaviour of the classical likelihood ratio test-statistic, as well asfor model misspecification problems, in both cases for the one population problem. This paper introduces this methodology for two sample problems. A simulation study illustrates situations in which the new test-statistics become a competitive tool with respect to the classical z-test and the likelihood ratio test-statistic.