  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.
