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.
Ritzwoller, Joseph P
4 Pith papers cite this work. Polarity classification is still indexing.
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A noise-based orthogonalization framework for valid hypothesis testing that extends to post-selection inference under mild assumptions.
Tutorial on a statistical roadmap and R packages for selective borrowing in hybrid controlled trials, demonstrated on synthetic lung cancer data.
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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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Testing hypotheses via orthogonalization
A noise-based orthogonalization framework for valid hypothesis testing that extends to post-selection inference under mild assumptions.
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Robust Estimation and Inference with Selective Borrowing in Hybrid Controlled Trials: A Tutorial with SelectiveIntegrative and intFRT
Tutorial on a statistical roadmap and R packages for selective borrowing in hybrid controlled trials, demonstrated on synthetic lung cancer data.
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