Quasi-complete separation

In a binary logistic regression, write the signed directional margin of observation $i$ as $(2y_i-1)x_i^{T}d$. Quasi-complete separation occurs when a nonzero direction $d$ makes all these margins nonnegative, with some zero and some strictly positive. Along coefficients $\beta +td$, $t\to \infty$, each observation's log-likelihood contribution is nondecreasing, and those with positive margins strictly increase toward their limiting value. Hence the unpenalized fit cannot have a finite maximum in that direction. A group containing only outcome zeros is a simple example: sending its indicator coefficient to $-\infty$ improves that group's likelihood while leaving all other observations unchanged. Zero fitted risk is then a boundary limit, and ordinary finite-coefficient Wald confidence intervals are unavailable.