  We report on a numerical evaluation of the statistical bootstrap as a
technique for radio-interferometric imaging fidelity assessment. The
development of a fidelity assessment technique is an important scientific
prerequisite for automated pipeline reduction of data from modern radio
interferometers. We evaluate the statistical performance of two bootstrap
methods, the model-based bootstrap and subsample bootstrap, against a Monte
Carlo parametric simulation, using interferometric polarization calibration and
imaging as the representative problem under study. We find both statistical
resampling techniques to be viable approaches to radio-interferometric imaging
fidelity assessment which merit further investigation. We also report on the
development and implementation of a new self-calibration algorithm for
radio-interferometric polarimetry which makes no approximations for the
polarization source model.
