English(EN) Sub-Gaussian Concentration and Entropic Normality of the Maximum Likelihood Estimator

新研究加强了最大似然估计量的渐近正态性

作者 PulseAugur 编辑部 · [2 个来源] · 2026-05-08 01:34

本文为最大似然估计量（MLE）引入了更强的渐近正态性形式。在特定的得分假设下，它建立了归一化估计误差的亚高斯尾界和所有矩的收敛性。该研究还证明了平滑MLE的熵中心极限定理，表明其在相对熵上收敛于高斯分布，并表明在某些条件下可以去除这种平滑。 AI

影响这项研究推进了统计估计的理论理解，可能影响依赖最大似然估计的更鲁棒的AI模型的开发。

排序理由该集群包含一篇在arXiv上发表的关于统计研究的学术论文。

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

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arXiv stat.ML TIER_1 English(EN) · Leighton P. Barnes, Alex Dytso · 2026-05-11 04:00

最大似然估计量的亚高斯集中与熵正则性

arXiv:2605.07107v1 Announce Type: cross Abstract: It is well known that, under standard regularity conditions, the maximum likelihood estimator (MLE) satisfies a central limit theorem and converges in distribution to a Gaussian random variable as the sample size grows. This paper…
arXiv stat.ML TIER_1 English(EN) · Alex Dytso · 2026-05-08 01:34

最大似然估计量的亚高斯集中与熵正则性

It is well known that, under standard regularity conditions, the maximum likelihood estimator (MLE) satisfies a central limit theorem and converges in distribution to a Gaussian random variable as the sample size grows. This paper strengthens this classical result by developing s…