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As the data Dh are drawn independently, identically with distribution P in P, we have assumed that δn/2 ≲ δn, ρn/2 ≲ ρn, by setting t = Cδn, Assumption A · 2023

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econ.EM 1

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2024 1

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Order-Explicit Linearization of High-Dimensional $U$-Statistics

econ.EM · 2024-05-13 · unverdicted · novelty 7.0

Derives an order-explicit large deviation bound for high-dimensional U-statistics from their Hájek projections, yielding new concentration results and consistency for resampling-based confidence intervals around subsampled kernel regression estimators.

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  • Order-Explicit Linearization of High-Dimensional $U$-Statistics econ.EM · 2024-05-13 · unverdicted · none · ref 20

    Derives an order-explicit large deviation bound for high-dimensional U-statistics from their Hájek projections, yielding new concentration results and consistency for resampling-based confidence intervals around subsampled kernel regression estimators.