Empirical Likelihood Ratio Test with Distribution Function Constraints

TL;DR

This paper introduces a new empirical likelihood ratio test utilizing distribution function constraints, enhancing detection accuracy in non-parametric hypothesis testing, especially when traditional moment constraints are inadequate.

Contribution

It proposes a novel distribution function constraint-based empirical likelihood ratio test that outperforms existing methods like Kolmogorov-Smirnov and Cramér-von Mises tests in detection problems.

Findings

The new test outperforms traditional goodness-of-fit tests in various detection scenarios.

Inclusion of noise distribution uncertainty improves detection performance.

The proposed test is asymptotically optimal.

Abstract

In this work, we study non-parametric hypothesis testing problem with distribution function constraints. The empirical likelihood ratio test has been widely used in testing problems with moment (in)equality constraints. However, some detection problems cannot be described using moment (in)equalities. We propose a distribution function constraint along with an empirical likelihood ratio test. This detector is applicable to a wide variety of robust parametric/non-parametric detection problems. Since the distribution function constraints provide a more exact description of the null hypothesis, the test outperforms the empirical likelihood ratio test with moment constraints as well as many popular goodness-of-fit tests, such as the robust Kolmogorov-Smirnov test and the Cram\'er-von Mises test. Examples from communication systems with real-world noise samples are provided to show their performance. Specifically, the proposed test significantly outperforms the robust Kolmogorov-Smirnov test and the Cram\'er-von Mises test when the null hypothesis is nested in the alternative hypothesis. The same example is repeated when we assume no noise uncertainty. By doing so, we are able to claim that in our case, it is necessary to include uncertainty in noise distribution. Additionally, the asymptotic optimality of the proposed test is provided.