  The problem of quickly diagnosing an unknown change in a stochastic process
is studied. We establish novel bounds on the performance of misspecified
diagnosis algorithms designed for changes that differ from those of the
process, and pose and solve a new robust quickest change diagnosis problem in
the asymptotic regime of few false alarms and false isolations. Simulations
suggest that our asymptotically robust solution offers a computationally
efficient alternative to generalised likelihood ratio algorithms.
