  The problem of quickest detection of a change in distribution is considered
under the assumption that the pre-change distribution is known, and the
post-change distribution is only known to belong to a family of distributions
distinguishable from a discretized version of the pre-change distribution. A
sequential change detection procedure is proposed that partitions the sample
space into a finite number of bins, and monitors the number of samples falling
into each of these bins to detect the change. A test statistic that
approximates the generalized likelihood ratio test is developed. It is shown
that the proposed test statistic can be efficiently computed using a recursive
update scheme, and a procedure for choosing the number of bins in the scheme is
provided. Various asymptotic properties of the test statistic are derived to
offer insights into its performance trade-off between average detection delay
and average run length to a false alarm. Testing on synthetic and real data
demonstrates that our approach is comparable or better in performance to
existing non-parametric change detection methods.
