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Basu, A. and Mandal, A. and Martín, N. and Pardo Llorente, Leandro (2015) Generalized Wald-type tests based on minimum density power divergence estimators. Statistics: A Journal of Theoretical and Applied Statistics . ISSN 0233-1888 (In Press)
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Official URL: http://www.tandfonline.com/loi/gsta20#.VS-ErvmsWCk
Abstract
In testing of hypothesis, the robustness of the tests is an important concern. Generally, the maximum likelihood-based tests are most efficient under standard regularity conditions, but they are highly non-robust even under small deviations from the assumed conditions. In this paper, we have proposed generalized Wald-type tests based on minimum density power divergence estimators for parametric hypotheses. This method avoids the use of nonparametric density estimation and the bandwidth selection. The trade-off between efficiency and robustness is controlled by a tuning parameter β. The asymptotic distributions of the test statistics are chi-square with appropriate degrees of freedom. The performance of the proposed tests is explored through simulations and real data analysis
Item Type: | Article |
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Uncontrolled Keywords: | density power divergence; robustness; tests of hypotheses |
Subjects: | Sciences > Mathematics > Mathematical statistics |
ID Code: | 29619 |
Deposited On: | 16 Apr 2015 09:46 |
Last Modified: | 16 Apr 2015 09:46 |
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