NP-EBCIs achieve asymptotic exact coverage for individual effects at logarithmic rates and shorten intervals in simulations compared to isolated treatment.
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The voting curve from repeated binary predictions is exactly equivalent to a signed voting signature capturing excess latent mass above the majority threshold at binomial variance scales, via signed Hausdorff moments.
NPMLE attains parametric rates for density and posterior mean estimation in bounded-support Gaussian/Poisson mixtures when mixing is finitely discrete, and the LRT is asymptotically tight iff the mixing distribution is finitely discrete.
Empirical Bayes rebiasing learns the bias distribution from paired noisy estimates to produce shorter calibrated intervals than full debiasing while maintaining coverage.
The paper claims empirical Bayes approaches target different inferential objects than full hierarchical posteriors and recommends redeploying modern computation toward the latter, illustrated via the Tweedie formula.
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Nonparametric Empirical Bayes Confidence Intervals
NP-EBCIs achieve asymptotic exact coverage for individual effects at logarithmic rates and shorten intervals in simulations compared to isolated treatment.