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

Research paper by Abhik Ghosh, Nirian Martin, Ayanendranath Basu, Leandro Pardo

Indexed on: 15 Feb '17Published on: 15 Feb '17Published in: arXiv - Statistics - Methodology



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.