Ghosh, AbhikMartín Apaolaza, NirianBasu, AyanendranathPardo Llorente, Leandro2024-07-032024-07-032018Ghosh, Abhik, Martin, Nirian, Basu, Ayanendranath and Pardo, Leandro. "A New Class of Robust Two-Sample Wald-Type Tests" The International Journal of Biostatistics, vol. 14, no. 2, 2018, pp. 20170023. https://doi.org/10.1515/ijb-2017-00231557-46792194-573X10.1515/ijb-2017-0023https://hdl.handle.net/20.500.14352/105575Parametric 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.engjournal articlehttps://doi.org/10.1515/ijb-2017-0023open accessRobust hypothesis testingTwo-sample problemsMinimum density power divergence estimatorInfluence functionClinical trialEstadística matemática (Matemáticas)2404.01 Bioestadística