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。
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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…
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…