# A new class of robust two-sample Wald-type tests

## TL;DR

This paper introduces robust Wald-type tests for two-sample parametric hypotheses, utilizing minimum density power divergence estimators to enhance robustness and applicability in fields like medicine and biology.

## Contribution

It develops novel Wald-type tests based on divergence estimators, providing asymptotic robustness for both simple and composite two-sample hypotheses.

## Key findings

- Tests are asymptotically robust and reliable.
- Numerical examples demonstrate improved performance over traditional methods.
- Applicable to clinical trial data and real-world biological datasets.

## Abstract

Parametric hypothesis testing associated with two independent samples arises frequently in several applications in biology, medical sciences, epidemiology, reliability and many more. In this paper, we propose robust Wald-type tests for testing such two sample problems using the minimum density power divergence estimators of the underlying parameters. In particular, we consider the simple two-sample hypothesis concerning the full parametric homogeneity of the samples as well as the general two-sample (composite) hypotheses involving nuisance parameters also. The asymptotic and theoretical robustness properties of the proposed Wald-type tests have been developed for both the simple and general composite hypotheses. Some particular cases of testing against one-sided alternatives are discussed with specific attention to testing the effectiveness of a treatment in clinical trials. Performances of the proposed tests have also been illustrated numerically through appropriate real data examples.