  We report on a broader evaluation of statistical bootstrap resampling methods
as a tool for pixel-level calibration and imaging fidelity assessment in radio
interferometry. Pixel-level imaging fidelity assessment is a challenging
problem, important for the value it holds in robust scientific interpretation
of interferometric images, enhancement of automated pipeline reduction systems
needed to broaden the user community for these instruments, and understanding
leadingedge direction-dependent calibration and imaging challenges for future
telescopes such as the Square Kilometer Array. This new computational approach
is now possible because of advances in statistical resampling for data with
long-range dependence and the available performance of contemporary
high-performance computing resources. We expand our earlier numerical
evaluation to span a broader domain subset in simulated image fidelity and
source brightness distribution morphologies. As before, we evaluate the
statistical performance of the bootstrap resampling methods against direct
Monte Carlo simulation. We find both model-based and subsample bootstrap
methods to continue to show significant promise for the challenging problem of
interferometric imaging fidelityassessment, when evaluated over the broader
domain subset. We report on their measured statistical performance and
guidelines for their use and application in practice. We also examine the
performance of the underlying polarization self-calibration algorithm used in
this study over a range of parallactic angle coverage.
