RT Journal Article
T1 Generalized Wald-type tests based on minimum density power divergence estimators
A1 Basu, A.
A1 Mandal, A.
A1 Martín, N.
A1 Pardo Llorente, Leandro
AB 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
PB Taylor & Francis Group Ltd
SN 0233-1888
YR 2015
FD 2015
LK https://hdl.handle.net/20.500.14352/33999
UL https://hdl.handle.net/20.500.14352/33999
LA eng
DS Docta Complutense
RD 31 dic 2025