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: A deviation study
2 Pith papers cite this work, alongside 279 external citations. Polarity classification is still indexing.
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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.
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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.
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Learning When to Trust LLM Priors: A Validated Framework for Semantic Prior Integration
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.