  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.
