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Generalized Wald-type Tests based on Minimum Density Power Divergence Estimators
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 .
The asymptotic distributions of the test statistics are chi-square with
appropriate degrees of freedom. The performance of the proposed tests are
explored through simulations and real data analysis.Comment: 26 pages, 10 figures. arXiv admin note: substantial text overlap with
arXiv:1403.033
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