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New research characterizes log-likelihood ratio statistics in logistic regression

By PulseAugur Editorial · [1 sources] ·

Researchers have characterized the finite sample behavior of the log-likelihood ratio statistic in binary logistic regression. Their findings provide a non-asymptotic analogue to the Wilks $\chi^2_d$ phenomenon, applicable without regularity assumptions on the design. The study reveals distinct behaviors for low-dimensional cases, with the worst-case quantile in dimension $d=2$ showing logarithmic dependence on $n$, and in dimension $d=1$, no dependence on $n$ at all. AI

IMPACT Provides theoretical underpinnings for statistical methods used in machine learning models.

RANK_REASON Academic paper detailing theoretical statistical findings. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New research characterizes log-likelihood ratio statistics in logistic regression

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Academic paper detailing theoretical statistical findings. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

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

    Beyond Modern Asymptotics for Log-Likelihood Ratios in Logistic Regression

    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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