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- W2040256584 abstract "When testing geometrically irregular parametric hypotheses, the bootstrap is an intuitively appealing method to circumvent difficult distribution theory. It has been shown, however, that the usual bootstrap is inconsistent in estimating the asymptotic distributions involved in such problems. This paper is concerned with the asymptotic size of likelihood ratio tests when critical values are computed using the inconsistent bootstrap. We clarify how the asymptotic size of such a test can be obtained from the size of the corresponding bootstrap test in the relevant limiting normal experiment. For boundary problems, that is, hypotheses given by convex cones, we show the bootstrap test to always be anticonservative, and we compute the size numerically for different two-dimensional examples. The examples illustrate that the size can be below or above the nominal level, and reveal that the relationship between the size of the test and the geometry of the considered hypotheses is surprisingly subtle." @default.
- W2040256584 created "2016-06-24" @default.
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- W2040256584 date "2011-10-02" @default.
- W2040256584 modified "2023-09-27" @default.
- W2040256584 title "Quantifying the failure of bootstrap likelihood ratio tests" @default.
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- W2040256584 doi "https://doi.org/10.1093/biomet/asr033" @default.
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