Optimal algorithms are developed for robust detection of changes in non-stationary processes. These are processes in which the distribution of the data after change varies with time. The decision-maker does not have access to precise information on the post-change distribution. It is shown that if the post-change non-stationary family has a distribution that is least favorable in a well-defined sense, then the algorithms designed using the least favorable distributions are robust and optimal. Non-stationary processes are encountered in public health monitoring and space and military applications. The robust algorithms are applied to real and simulated data to show their effectiveness.

翻译：针对非平稳过程中的变化鲁棒检测问题，本文开发了最优算法。此类过程中，变化发生后数据的分布会随时间动态变化，且决策者无法获取变化后分布的精确信息。研究表明，若变化后的非平稳分布族中存在一个在明确定义意义下最不利的分布，则基于该最不利分布设计的算法兼具鲁棒性与最优性。非平稳过程常见于公共卫生监测、航天及军事应用领域。通过真实数据与模拟数据的实验验证，本文提出的鲁棒算法展现了其有效性。