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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3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
Redefining the likelihood on the model family M rather than the parameter space causes the strong likelihood principle to collapse into the weak likelihood principle.
The support function of the identified set for solutions to conditional linear programs is expressed as an average of intersections of regression functions and shown to be a regular parameter admitting standard asymptotic inference.
citing papers explorer
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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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Strong Likelihood Principle: Strengthening a Principle or Misunderstanding the Likelihood Function
Redefining the likelihood on the model family M rather than the parameter space causes the strong likelihood principle to collapse into the weak likelihood principle.
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Adaptive Estimation of Aggregated Values of Conditional Linear Programs
The support function of the identified set for solutions to conditional linear programs is expressed as an average of intersections of regression functions and shown to be a regular parameter admitting standard asymptotic inference.