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.
See Chapter 1 of De la Pena and Gin´e (1999) for a textbook treatment
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Order-Explicit Linearization of High-Dimensional $U$-Statistics
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.