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- W2087029205 abstract "This paper studies the Generalized Neyman–Pearson (GNP) optimality of empirical likelihood-based tests for parameter hypotheses. The GNP optimality focuses on the large deviation errors of tests, i.e., the convergence rates of the type I and II error probabilities under fixed alternatives. We derive (i) the GNP optimality of the empirical likelihood criterion (ELC) test against all alternatives, and (ii) a necessary and a sufficient condition for the GNP optimality of the empirical likelihood ratio (ELR) test against each alternative." @default.
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- W2087029205 date "2008-04-24" @default.
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- W2087029205 title "Generalized Neyman–Pearson optimality of empirical likelihood for testing parameter hypotheses" @default.
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- W2087029205 doi "https://doi.org/10.1007/s10463-008-0172-6" @default.
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