  Optimal algorithms are developed for robust detection of changes in
non-stationary processes. These are processes in which the distribution of the
data after change varies with time. The decision-maker does not have access to
precise information on the post-change distribution. It is shown that if the
post-change non-stationary family has a distribution that is least favorable in
a well-defined sense, then the algorithms designed using the least favorable
distributions are robust and optimal. Non-stationary processes are encountered
in public health monitoring and space and military applications. The robust
algorithms are applied to real and simulated data to show their effectiveness.
