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Overstuffed sandwiches and separation anxiety: finite-sample variance estimation for penalized GEE with near-separated binary data
[Submitted on 20 Apr 2026 (v1), last revised 13 Jun 2026 (this v · 2026-06-16 · via stat updates on arXiv.org

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Abstract:Penalized generalized estimating equations (PGEE) stabilize point estimation for longitudinal binary data under near-separation, but inference still depends on how the sandwich variance is corrected. Existing corrections for PGEE can overadjust in high-leverage directions, require restrictive pooling assumptions, or add global regularization without explaining the bias. We establish first-order asymptotics for PGEE along convergent interior-root sequences and derive a matrix characterization of the parameter-specific overcorrection induced by full leverage adjustment. Finite-sample calibration is limited by both mean bias and the variability of leverage-corrected variance estimates. We propose $\hat{V}_{AR}$, which keeps the score-level leverage correction and adds a finite-sample upward translation dominated at first order by the finite-population factor, with a smaller centering term. In simulations, $\hat{V}_{AR}$ gives conservative or near-nominal type I error in low-event, small-$N$ settings, including $N = 10$, where several standard corrections remain anti-conservative and pooling estimators are unavailable for unbalanced designs.

Submission history

From: Awan Afiaz [view email]
[v1] Mon, 20 Apr 2026 21:39:16 UTC (303 KB)
[v2] Sat, 13 Jun 2026 16:03:52 UTC (304 KB)