Extends second-order bootstrap theory from maxima to the k-th order statistic in high dimensions via factorial moments and inclusion-exclusion, achieving n^{-1} coverage error with third-moment matching wild bootstrap.
Gaussian Multiplier Bootstrap Procedure for the k-th Largest Coordinate of High - Dimensional Statistics
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A framework that uses LLM sparse autoencoder features as high-dimensional outcome variables, with new k-FWER controlling bootstrap tests and formalized autointerp evaluation, to make interpretable discoveries from unstructured data.
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High Dimensional Bootstrap and Asymptotic Expansion for the $k$-th Largest Coordinate
Extends second-order bootstrap theory from maxima to the k-th order statistic in high dimensions via factorial moments and inclusion-exclusion, achieving n^{-1} coverage error with third-moment matching wild bootstrap.
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Making Interpretable Discoveries from Unstructured Data: A High-Dimensional Multiple Hypothesis Testing Approach
A framework that uses LLM sparse autoencoder features as high-dimensional outcome variables, with new k-FWER controlling bootstrap tests and formalized autointerp evaluation, to make interpretable discoveries from unstructured data.