How uncertain is a finite-sample retention estimate?

Develop influence-function or bootstrap confidence intervals for reported retention functionals (e.g. geometric-mean retention), handling the non-smoothness of hard assignment at cell boundaries, so retention is reported with error bars rather than as a point estimate. Settled special cases under a frozen rule with an iid equally weighted oracle-score evaluation sample: scalar true score (5 Sep 2026, RETENTION-PLUGIN-CLT-FROZEN-SCALAR, independently audited) and d-dimensional true score with the geometric-mean D-retention (det I_Z / det V)^(1/d) (6 Sep 2026, RETENTION-PLUGIN-CLT-FROZEN-VECTOR, independently audited the same day) - the plug-in is asymptotically normal with an explicit matrix influence function, a consistent plug-in variance and a valid Wald interval iff sigma^2 > 0, where sigma^2 = 0 iff every cell's conditional law sits on an ellipsoid (CE-O7-ELLIPSOID-ZERO-VARIANCE-001 shows atomless such laws exist for d >= 2), and at singular I_Z the plug-in is biased upward at rate n^{-(d-r)/d} (RETENTION-PLUGIN-SINGULAR-ENDPOINT-RATE). Still open: rules refitted on the evaluation sample, where the boundary non-smoothness enters; weighted samples; any uncertainty statement without an oracle score; the profiled D_s retention (a Schur-complement functional); the degenerate limits of n(eta_hat - eta) when sigma^2 = 0 and the joint limit of (n^{(d-r)/d} eta_hat_D, n^{(d-r)/d} sigma_hat/sqrt(n)) at the singular endpoint (the endpoint coverage is measured to stabilise, not proved to converge); and a second-order-corrected or transformed interval for heavy-tailed scores, where the first-order interval is measured to be liberal at n <= 3000.