We consider sequential change-point detection in parallel data streams, where each stream has its own change point. Once a change is detected in a data stream, this stream is deactivated permanently. The goal is to maximize the normal operation of the pre-change streams, while controlling the proportion of post-change streams among the active streams at all time points. Taking a Bayesian formulation, we develop a compound decision framework for this problem. A procedure is proposed that is uniformly optimal among all sequential procedures which control the expected proportion of postchange streams at all time points. We also investigate the asymptotic behavior of the proposed method when the number of data streams grows large. Numerical examples are provided to illustrate the use and performance of the proposed method.

翻译：我们考虑在平行数据流中进行顺序变化点检测,每个数据流都有自己的变化点。 一旦在数据流中检测到变化, 就会永久停止该流。 目标是最大限度地实现变化前流的正常运行, 同时在所有时间点控制活动流中的变化后流的比例。 采用巴耶斯式的配方, 我们为此问题制定一个复合决定框架。 提出了一个程序, 在所有连续程序中统一优化, 在所有时间点控制变化后流的预期比例 。 我们还调查在数据流数量大幅增长时拟议方法的无药可治行为。 提供了数字示例, 以说明拟议方法的使用情况和性能 。