Basu, A.Mandal, A.Martín, N.Pardo Llorente, Leandro2023-06-192023-06-1920150233-1888https://hdl.handle.net/20.500.14352/33999In 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 analysisengjournal articlehttp://www.tandfonline.com/loi/gsta20#.VS-ErvmsWCkhttp://arxiv.org/pdf/1403.7616v3.pdfopen access519.22density power divergencerobustnesstests of hypothesesEstadística matemática (Matemáticas)1209 Estadística