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New research strengthens asymptotic normality for maximum likelihood estimators

By PulseAugur Editorial · Summary by gemini-2.5-flash-lite from 2 sources

This paper introduces stronger forms of asymptotic normality for the maximum likelihood estimator (MLE). It establishes sub-Gaussian tail bounds and convergence of all moments for the normalized estimation error under specific score assumptions. The research also proves an entropic central limit theorem for a smoothed MLE, demonstrating convergence in relative entropy to a Gaussian law, and shows this smoothing can be removed under certain conditions. AI

Summary written by gemini-2.5-flash-lite from 2 sources. How we write summaries →

IMPACT This research advances theoretical understanding in statistical estimation, potentially impacting the development of more robust AI models that rely on maximum likelihood estimation.

RANK_REASON The cluster contains an academic paper published on arXiv detailing statistical research.

Read on arXiv stat.ML →

COVERAGE [2]

  1. arXiv stat.ML TIER_1 · Leighton P. Barnes, Alex Dytso ·

    Sub-Gaussian Concentration and Entropic Normality of the Maximum Likelihood Estimator

    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…

  2. arXiv stat.ML TIER_1 · Alex Dytso ·

    Sub-Gaussian Concentration and Entropic Normality of the Maximum Likelihood Estimator

    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…

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