This paper introduces a new stabilized higher-order estimator for statistical functionals, aiming to improve upon existing methods that suffer from numerical instability or require complex density estimation. The proposed approach bypasses sample splitting and density estimation, offering more stable finite-sample performance while maintaining similar statistical guarantees. The work builds upon previous research in higher-order influence functions, which provide a unified framework for constructing rate-optimal point estimates. AI
IMPACT This research could lead to more stable and efficient statistical modeling techniques, potentially impacting AI development in areas requiring complex data analysis.
RANK_REASON The cluster contains an academic paper published on arXiv detailing a new statistical theory and estimator.
- alphaXiv
- arXiv
- CatalyzeX
- DagsHub
- Gotit.pub
- Hugging Face
- Influence Flower
- Liu
- ScienceCast
- van der Vaart
- Robins et al.
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