  The density band model proposed by Kassam for robust hypothesis testing is
revisited in this paper. First, a novel criterion for the general
characterization of least favorable distributions is proposed, which unifies
existing results. This criterion is then used to derive an implicit definition
of the least favorable distributions under band uncertainties. In contrast to
the existing solution, it only requires two scalar values to be determined and
eliminates the need for case-by-case statements. Based on this definition, a
generic fixed-point algorithm is proposed that iteratively calculates the least
favorable distributions for arbitrary band specifications. Finally, three
different types of robust tests that emerge from band models are discussed and
a numerical example is presented to illustrate their potential use in practice.
