We develop a mixture procedure to monitor parallel streams of data for a
change-point that affects only a subset of them, without assuming a spatial
structure relating the data streams to one another. Observations are assumed
initially to be independent standard normal random variables. After a
change-point the observations in a subset of the streams of data have nonzero
mean values. The subset and the post-change means are unknown. The procedure we
study uses stream specific generalized likelihood ratio statistics, which are
combined to form an overall detection statistic in a mixture model that
hypothesizes an assumed fraction $p_0$ of affected data streams. An analytic
expression is obtained for the average run length (ARL) when there is no change
and is shown by simulations to be very accurate. Similarly, an approximation
for the expected detection delay (EDD) after a change-point is also obtained.
Numerical examples are given to compare the suggested procedure to other
procedures for unstructured problems and in one case where the problem is
assumed to have a well-defined geometric structure. Finally we discuss
sensitivity of the procedure to the assumed value of $p_0$ and suggest a
generalization.

本文提出了一种混合过程，通过使用特定于流的广义似然比统计量来跟踪并检测数据流中的变化点，我们假定一开始的数据为独立标准正态随机变量。在变化点后，数据流的子集将具有非零平均值，这是我们需要检测和跟踪的。我们在分析中提出了 ARL 和 EDD 测量方法，通过数值实验得到了良好的结果。