  A data-driven convergence criterion for the D'Agostini (Richardson-Lucy)
iterative unfolding is presented. It relies on the unregularized spectrum
(infinite number of iterations), and allows a safe estimation of the bias and
undercoverage induced by truncating the algorithm. In addition, situations
where the response matrix is not perfectly known are also discussed, and show
that in most cases the unregularized spectrum is not an unbiased estimator of
the true distribution. Whenever a bias is introduced, either by truncation of
by poor knowledge of the response, a way to retrieve appropriate coverage
properties is proposed.
