= Quasi-complete separation
In a binary <logistic regression>, write the signed directional margin of observation $i$ as $(2y_i-1)x_i^Td$. 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.
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