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New research characterizes log-likelihood ratio statistics in logistic regression

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员表征了二元逻辑回归中对数似然比统计量的有限样本行为。他们的发现为 Wilks $\chi^2_d$ 现象提供了一个非渐近的类似物，适用于设计上的正则性假设。该研究揭示了低维情况下的不同行为，其中 $d=2$ 维度的最坏情况分位数显示出对 $n$ 的对数依赖性，而在 $d=1$ 维度上，则完全不依赖于 $n$。 AI

影响 为机器学习模型中使用的统计方法提供了理论基础。

排序理由 学术论文，详细介绍理论统计发现。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv cs.LG 阅读 →

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New research characterizes log-likelihood ratio statistics in logistic regression

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学术论文，详细介绍理论统计发现。[lever_c_demoted from research: ic=1 ai=0.7]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Hugo Chardon, Reese Pathak, Nikita Zhivotovskiy ·

    Logistic回归中对数似然比的超越渐近分析

    arXiv:2608.02507v1 Announce Type: cross Abstract: We characterize the finite sample behavior of the log-likelihood ratio statistic in binary logistic regression, uniformly over both the design and the target parameter. For $n\geq d\geq 3$, we determine, up to universal constants,…

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