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.

中文翻译：

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错误指定和渐近极小极大鲁棒最快变化诊断

研究快速诊断随机过程中未知变化的问题。我们为错误指定的诊断算法的性能建立了新的界限，该算法专为与过程不同的变化而设计，并在几乎没有假警报和假隔离的渐近状态下提出并解决了一个新的鲁棒的最快变化诊断问题。模拟表明，我们的渐近鲁棒解决方案提供了一种通用似然比算法的计算高效替代方案。