  Detecting an abrupt and persistent change in the underlying distribution of
online data streams is an important problem in many applications. This paper
proposes a new robust score-based algorithm called RSCUSUM, which can be
applied to unnormalized models and addresses the issue of unknown post-change
distributions. RSCUSUM replaces the Kullback-Leibler divergence with the Fisher
divergence between pre- and post-change distributions for computational
efficiency in unnormalized statistical models and introduces a notion of the
``least favorable'' distribution for robust change detection. The algorithm and
its theoretical analysis are demonstrated through simulation studies.
