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.

翻译：在线数据流底层分布中突发持续性变化的检测是众多应用中的重要问题。本文提出一种新型鲁棒基于分数的算法RSCUSUM，可应用于非规范化模型并解决未知变化后分布的问题。RSCUSUM通过使用Fisher散度替代预变化与后变化分布之间的Kullback-Leibler散度，在非规范化统计模型中实现计算效率优化，并引入“最不利”分布概念以实现鲁棒变化检测。通过仿真实验论证了该算法及其理论分析。