Local privacy mechanisms preserve rate-double-robustness, enabling unbiased and semiparametrically efficient inference on target parameters indexed linearly by infinite-dimensional and nonlinearly by low-dimensional components from noisy private data.
A Remark on Stirling's Formula
4 Pith papers cite this work, alongside 531 external citations. Polarity classification is still indexing.
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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.
The paper characterizes the worst-case expected top-k norm of sample averages for heavy-tailed vectors up to universal constants under envelope moment conditions.
Scaling MLN weights by 1/n induces a weight-independent 0-1 law for FO logic; unscaled weights produce seven regimes with possible phase transitions and convergence laws.
citing papers explorer
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Private Rate-Double-Robust Inference
Local privacy mechanisms preserve rate-double-robustness, enabling unbiased and semiparametrically efficient inference on target parameters indexed linearly by infinite-dimensional and nonlinearly by low-dimensional components from noisy private 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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Worst-Case Maximal Inequalities for Heavy-tailed Random Vectors
The paper characterizes the worst-case expected top-k norm of sample averages for heavy-tailed vectors up to universal constants under envelope moment conditions.
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Random coloured digraphs defined by a Markov logic network
Scaling MLN weights by 1/n induces a weight-independent 0-1 law for FO logic; unscaled weights produce seven regimes with possible phase transitions and convergence laws.