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New research strengthens asymptotic normality for maximum likelihood estimators

By PulseAugur Editorial · [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

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 →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New research strengthens asymptotic normality for maximum likelihood estimators

COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · 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 English(EN) · 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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