论文标题
最快的变化检测，并估计一对一的密度估计
Quickest Change Detection with Leave-one-out Density Estimation
论文作者
论文摘要
考虑了一系列独立观察的最快变化检测问题。假定更改前分布是已知的，而后变化分布是完全未知的。开发了一个窗户限制的剩余（LOO）cusum测试，该测试对变换后分布没有任何了解，并且不需要任何变化后的培训样本。结果表明，在密度估计器上的某些收敛条件下，较高的cusum测试在渐近上是最佳的，因为错误警报速率为零。分析通过数值结果验证，其中比较了Loo-Cusum测试与具有分布知识的基线测试。
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.
