The Lindley process defined for the queuing file domain is equivalent to the cumulative sum (CUSUM) process used for break‐point detection in process control. The maximum of the Lindley process, called local score, is used to highlight atypical regions in biological sequences, and its distribution has been established by different manners. I propose here to use the local score and also a partial maximum of the Lindley process over the immediate past to create control charts. Stopping time corresponds to the first time where the statistic achieves a statistical significance less than a given threshold α in ]0,1[, the instantaneous first error rate. The local score p value is computed using existing theoretical results. I establish here the exact distribution of the partial maximum of the Lindley process. Performance of the control charts is evaluated by Monte Carlo estimation of the average run lengths for an in‐control process (ARL₀) and for an out‐of‐control process (ARL₁). I also use the standard deviation of the run length (SdRL) and the extra quadratic loss (EQL). Comparison with the usual and recent control charts present in the literature shows that the local score control chart outperforms the others with a much larger ARL₀ and ARL₁ smaller or of the same order. Many interesting openings exist for the local score chart: not only Gaussian model but also any of them, Markovian dependance of the data, both location and dispersion monitoring at the same time can be considered.

中文翻译：

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将生物序列分析工具转移到用于在线监测的断点检测：基于本地评分的控制图

为排队文件域定义的Lindley流程等效于在流程控制中用于断点检测的累积和（CUSUM）流程。Lindley过程的最大值称为局部评分，用于突出显示生物序列中的非典型区域，并且已通过不同方式确定了其分布。我在这里建议使用局部得分以及最近一段时间的Lindley过程的部分最大值来创建控制图。停止时间对应于首次统计达到统计显着性的第一时间，该统计显着性小于瞬时第一错误率[0,1]中的给定阈值α。本地分数p使用现有理论结果计算值。我在这里确定Lindley过程的部分最大值的确切分布。控制图的性能通过蒙特卡洛估计法对控制中过程（A R L ₀）和失控过程（A R L ₁）的平均游程长度进行评估。我还使用行程的标准偏差（S d R L）和额外的二次损失（E Q L）。与文献中常见的和最近的控制图进行比较后，可以看出，本地分数控制图的A值要大得多，其其他控制图R L ₀和A R L ₁较小或相同。局部计分图存在许多有趣的空缺：不仅可以考虑高斯模型，还可以考虑其中的任何一个，可以考虑数据的马尔可夫依赖关系，可以同时进行位置和色散监控。