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- W2023984598 abstract "Robust tests for linear models are derived via Wald-type tests that are based on asymptotically linear estimators. For a robustness criterion, the maximum asymptotic bias of the level of the test for distributions in a shrinking contamination neighborhood is used. By also regarding the asymptotic power of the test, admissible robust tests and most-efficient robust tests are derived. For the greatest efficiency, the determinant of the covariance matrix of the underlying estimator is minimized. Also, most-robust tests are derived. It is shown that at the classical $D$-optimal designs, the most-robust tests and the most-efficient robust tests have a very simple form. Moreover, the $D$-optimal designs provide the highest robustness and the highest efficiency under robustness constraints across all designs. So, $D$-optimal designs are also the optimal designs for robust testing. Two examples are considered for which the most-robust tests and the most-efficient robust tests are given." @default.
- W2023984598 created "2016-06-24" @default.
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- W2023984598 date "1998-06-01" @default.
- W2023984598 modified "2023-09-27" @default.
- W2023984598 title "Optimum robust testing in linear models" @default.
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- W2023984598 doi "https://doi.org/10.1214/aos/1024691091" @default.
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