New research characterizes log-likelihood ratio statistics in logistic regression

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 →

• chi-squared distribution
• Gaussian function
• logistic regression model
• Wilks

• paper
• other

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