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New research explores minimax rates for powered Sliced Wasserstein functionals

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This paper delves into the statistical estimation of powered Sliced Wasserstein functionals, particularly focusing on scenarios with two distinct sample sizes. The research establishes minimax rates for estimation under specific moment assumptions, detailing how these rates vary based on the relationship between sample sizes and moment orders. The study also identifies limitations, such as the impossibility of uniformly consistent estimation at the endpoint $r=p$ and an open question regarding the exact minimax rate in the critical case $r=2p$. AI

RANK_REASON The cluster contains a single academic paper detailing statistical theory and methods. [lever_c_demoted from research: ic=1 ai=0.4]

Read on arXiv stat.ML →

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New research explores minimax rates for powered Sliced Wasserstein functionals

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The cluster contains a single academic paper detailing statistical theory and methods. [lever_c_demoted from research: ic=1 ai=0.4]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Binh Thuan Tran ·

    Minimax Rates for Estimating Powered Sliced Wasserstein Functionals

    arXiv:2610.12740v1 Announce Type: cross Abstract: We study the two-sample estimation of the powered Sliced Wasserstein functional under projected $r$-th moment assumptions. Let $N=n\wedge m$, where $n$ and $m$ are two sample sizes. We establish the minimax rates in the noncritica…

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