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：本文针对非平稳过程中的变化检测问题，提出了最优的鲁棒检测算法。此类过程中的数据分布在变化后随时间动态变化。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在某种明确定义的意义上存在一个最不利分布，则基于该最不利分布设计的算法具有鲁棒性和最优性。非平稳过程常见于公共卫生监测、航天及军事应用等领域。通过实际数据与仿真实验验证了所提鲁棒算法的有效性。