PulseAugur
EN
LIVE 08:38:55

New Jackknife Variance Estimator for Generalized U-Statistics

By PulseAugur Editorial · [1 sources] ·

A new paper by Jakob Juergens introduces the jackknife variance estimator for a broad class of generalized U-statistics. The research proves ratio-consistency for this estimator and its delete-$d$ variants, unifying and generalizing existing criteria. This work clarifies the theoretical justification for the nonparametric jackknife in generalized settings and improves variance estimation for specific regression estimators under weaker conditions. AI

RANK_REASON Academic paper published on arXiv detailing statistical methodology. [lever_c_demoted from research: ic=1 ai=0.1]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New Jackknife Variance Estimator for Generalized U-Statistics

How we ranked this

Signal score
0 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket
Tool
Academic paper published on arXiv detailing statistical methodology. [lever_c_demoted from research: ic=1 ai=0.1]
Source corroboration
Single-source cluster
Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics
paper, other
Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance
Low
Off-topic or adjacent — cluster remains reachable but doesn't surface in AI-industry rankings.
Story freshness
77 days old
Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.

Full methodology in our editorial standards.

COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Jakob R. Juergens ·

    Jackknife Variance Estimation for H\'ajek-Dominated Generalized U-Statistics

    arXiv:2509.12356v2 Announce Type: replace-cross Abstract: Valid uncertainty quantification for subsampling-based and randomized estimators often depends on variance estimators whose behavior is much less understood than that of the underlying point estimator. We prove ratio-consi…

RELATED ENTITIES

RELATED TOPICS