RT Journal Article
T1 A New Class of Robust Two-Sample Wald-Type Tests
A1 Ghosh, Abhik
A1 Martín Apaolaza, Nirian
A1 Basu, Ayanendranath
A1 Pardo Llorente, Leandro
AB 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.
PB De Gruyter
SN 1557-4679
SN 2194-573X
YR 2018
FD 2018
LK https://hdl.handle.net/20.500.14352/105575
UL https://hdl.handle.net/20.500.14352/105575
LA eng
NO 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
DS Docta Complutense
RD 21 ago 2024