For mean-zero unit-variance random variables with fourth moment at most κ, the sharp one-sided tail V₁(t,κ) is completely mapped into four explicit regimes, with matching certificates and a proof-degree phase transition.
Challenging the Empirical Mean and Empirical Variance:
3 Pith papers cite this work, alongside 279 external citations. Polarity classification is still indexing.
years
2026 3representative citing papers
HELPI is a hyperbolic latent position model for binary bipartite data whose identified geometric target is the root-invariant projected Gromov product, with contraction theory and variational computation.
Statsformer adaptively integrates LLM semantic priors into a library of predictors via out-of-fold validation, delivering an oracle-style guarantee that the final predictor performs no worse than the best convex combination of its candidates up to statistical error.
citing papers explorer
-
The Exact Worst-Case Tail Probability under Bounded Kurtosis
For mean-zero unit-variance random variables with fourth moment at most κ, the sharp one-sided tail V₁(t,κ) is completely mapped into four explicit regimes, with matching certificates and a proof-degree phase transition.