The problem of quickest change detection in a sequence of independent observations is considered. The pre-change distribution is assumed to be known, while the post-change distribution is completely unknown. A window-limited leave-one-out (LOO) CuSum test is developed, which does not assume any knowledge of the post-change distribution, and does not require any post-change training samples. It is shown that, with certain convergence conditions on the density estimator, the LOO-CuSum test is first-order asymptotically optimal, as the false alarm rate goes to zero. The analysis is validated through numerical results, where the LOO-CuSum test is compared with baseline tests that have distributional knowledge.

翻译：考虑了在一系列独立观测中最迅速发现变化的问题。假设变化前分布为已知的,而变化后分布则完全未知。开发了一个窗口限制放假一出(LOO) Cusum 测试,该测试不假定对变化后分布有任何了解,也不要求任何变化后培训样本。据证明,根据密度测算器的某些趋同条件,LOO-CuSum 测试是第一阶最佳的,因为假警报率降至零。分析通过数字结果进行验证,即LOO-CuSum 测试与具有分布知识的基线测试进行比较。