  The quasi-optimality criterion chooses the regularization parameter in
inverse problems without taking into account the noise level. This rule works
remarkably well in practice, although Bakushinskii has shown that there are
always counterexamples with very poor performance. We propose an average case
analysis of quasi-optimality for spectral cut-off estimators and we prove that
the quasi-optimality criterion determines estimators which are rate-optimal
{\em on average}. Its practical performance is illustrated with a calibration
problem from mathematical finance.
