%0 Journal Article
%A Basu, A.
%A Mandal, A.
%A Martín, N.
%A Pardo Llorente, Leandro
%T Generalized Wald-type tests based on minimum density power divergence estimators
%D 2015
%@ 0233-1888
%U https://hdl.handle.net/20.500.14352/33999
%X 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
%~