  We propose using the frequency-domain bootstrap (FDB) to estimate errors of
modeling parameters when the modeling error is itself a major source of
uncertainty. Unlike the usual bootstrap or the simple $\chi^2$ analysis, the
FDB can take into account correlations between errors. It is also very fast
compared to the the Gaussian process Bayesian estimate as often implemented for
computer model calibration. The method is illustrated drop model of nuclear
binding energies. We find that the FDB gives a more conservative estimate of
the uncertainty in liquid drop parameters in better accord with more empirical
estimates. For the nuclear physics application, there no apparent obstacle to
apply the method to the more accurate and detailed models based on
density-functional theory.
