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 as well as the general two-sample (composite) hypotheses involving some nuisance parameters. 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.

中文翻译：

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一类新型的稳健的两样本Wald型检验。

与两个独立样本相关的参数假设检验在生物学，医学，流行病学，可靠性等领域的许多应用中经常出现。在本文中，我们提出了鲁棒的Wald型检验，以使用基础参数的最小密度功率散度估计量来测试这两个样本问题。特别是，我们考虑了关于完全参数同质性的简单两样本假设以及涉及某些令人讨厌的参数的一般两样本（复合）假设。已针对简单和一般的复合假设开发了拟议的Wald型检验的渐近和理论鲁棒性。讨论了一些针对单方面方案的特定测试案例，并特别关注在临床试验中测试某种疗法的有效性。还通过适当的实际数据示例以数字方式说明了建议的测试的性能。