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math.ST2026
Beyond Modern Asymptotics for Log-Likelihood Ratios in Logistic Regression
Hugo Chardon, Reese Pathak, Nikita Zhivotovskiy
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…
math.ST2026
Minimum Norm Interpolation via The Local Theory of Banach Spaces: The Role of Gaussianity
Gil Kur, Reese Pathak
We study minimum-norm interpolation (MNI) in overparameterized linear regression with isotropic Gaussian covariates, in settings where the MNI has no closed-form formula. Whereas m…
math.ST2025
Revisiting mean estimation over balls: Is the MLE optimal?
Liviu Aolaritei, Michael I. Jordan, Reese Pathak +1
We revisit the problem of mean estimation in the Gaussian sequence model with constraints for . We demonstrate two phenomena for the behavior of the max…