Power in Monte Carlo permutation tests is non-monotonic and can decrease with more sampled permutations, with such decreases occurring infinitely often due to distributional discreteness.
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A generalization of the Benjamini-Hochberg procedure controls the FDR curve below any specified level in location families, and the standard procedure simultaneously controls the entire curve for free.
Sign-flipping of individual score contributions yields asymptotically valid confidence intervals for GLMs under arbitrary variance misspecification.
citing papers explorer
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More Permutations Do Not Always Increase Power: Non-monotonicity in Monte Carlo Permutation Tests
Power in Monte Carlo permutation tests is non-monotonic and can decrease with more sampled permutations, with such decreases occurring infinitely often due to distributional discreteness.
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Simultaneous false discovery rate control in location families
A generalization of the Benjamini-Hochberg procedure controls the FDR curve below any specified level in location families, and the standard procedure simultaneously controls the entire curve for free.
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Robust confidence intervals for generalized linear models
Sign-flipping of individual score contributions yields asymptotically valid confidence intervals for GLMs under arbitrary variance misspecification.