Beyond Modern Asymptotics for Log-Likelihood Ratios in Logistic Regression

• URL: http://arxiv.org/abs/2608.02507v1
• Date: Mon, 03 Aug 2026 17:07:41 GMT
• Title: Beyond Modern Asymptotics for Log-Likelihood Ratios in Logistic Regression
• Abstract summary: We characterize the finite sample behavior of the log-likelihood ratio statistic in binary logistic regression.<n>For $ngeq dgeq 3$, we determine, up to universal constants, its worst case $(1)$ quantile over all fixed collections of design vectors and all target parameters.
• Score: 8.825582352614797
• License: http://creativecommons.org/licenses/by/4.0/
• 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, its worst case $(1-δ)$ quantile over all fixed collections of design vectors and all target parameters: \[ d\log\left(\frac{e n}{d}\right)+\log\left(\frac{1}δ\right). \] This is a nonasymptotic analogue of the Wilks $χ^2_d$ phenomenon and requires no regularity assumptions on the design. The low dimensional cases exhibit unusual behavior. The worst case quantile in dimension $d=2$ is sharply of order \[ \log\log\log n+\log\left(\frac{1}δ\right). \] The worst case quantile in dimension $d=1$ is of order $\log(1/δ)$, with no dependence on $n$. Finally, i.i.d. Gaussian design vectors recover the classical Wilks scale. In the regime $n\gtrsim d+\log(1/δ)$, we prove the sharp bound \[ d+\log\left(\frac{1}δ\right). \] Unlike existing asymptotic results, our bounds are uniform over the target parameter, which may depend on $n$, $d$, and $δ$.

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