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.
Central limit theorems and bootstrap in high dimensions
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A new framework combines AI-derived concept embeddings with high-dimensional selective inference to enable statistically principled, interpretable discovery from unstructured data in empirical economics.
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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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Making Interpretable Discoveries from Unstructured Data: A High-Dimensional Multiple Hypothesis Testing Approach
A new framework combines AI-derived concept embeddings with high-dimensional selective inference to enable statistically principled, interpretable discovery from unstructured data in empirical economics.