A New Class of Robust Two-Sample Wald-Type Tests

Ghosh, Abhik; Martín Apaolaza, Nirian; Basu, Ayanendranath; Pardo Llorente, Leandro

A New Class of Robust Two-Sample Wald-Type Tests

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TwoSampleWaldType.pdf (2.11 MB)

Official URL

https://doi.org/10.1515/ijb-2017-0023

Publication date

2018

Authors

Ghosh, Abhik

Martín Apaolaza, Nirian

Basu, Ayanendranath

Pardo Llorente, Leandro

Publisher

De Gruyter

Citations

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URI

https://hdl.handle.net/20.500.14352/105575

Citation

Ghosh, 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-2017-0023

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 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.