In this paper, some efficient monitoring and post-signal follow-up approaches are studied and compared for joint surveillance of location and scale of a process using the notions of circular-grid (CG) schemes. Precisely, three variants of CG Cucconi schemes are introduced and compared with three variants of percentile modified Lepage (PML) schemes. One of the PML schemes is equivalent to the traditional CG Lepage scheme, while another may be viewed as the Lepage type statistic using Gastwrith score, which is also a powerful tool for process surveillance. Overall, one of the proposed CG Cucconi schemes is most effective in identifying a class of signals, whether it is a location shift or scale shift or a shift in both parameters. It also indicates the direction of the shifts in either or both the parameters. Detecting a downward scale shift is the most challenging task in joint monitoring, and to this end, a new bias-corrected CG Lepage scheme is introduced. We compare the competing schemes in terms of correct signal classification probabilities. We illustrate the use of the proposed schemes in monitoring the trip-duration data in cab services. Some concluding remarks and future research problems are offered.

中文翻译：

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一类新的用于信号分类的非参数圆网格图

在本文中，使用圆形网格 (CG) 方案的概念，研究和比较了一些有效的监控和信号后跟踪方法，用于联合监视过程的位置和规模。准确地说，介绍了 CG Cucconi 方案的三个变体，并与百分位修正 Lepage (PML) 方案的三个变体进行了比较。其中一种 PML 方案等效于传统的 CG Lepage 方案，而另一种方案可以视为使用 Gastwrith 分数的 Lepage 类型统计量，这也是过程监控的强大工具。总体而言，提出的 CG Cucconi 方案之一在识别一类信号方面最有效，无论是位置偏移、尺度偏移还是两个参数的偏移。它还指示一个或两个参数的变化方向。检测向下的尺度偏移是联合监测中最具挑战性的任务，为此，引入了一种新的偏差校正 CG Lepage 方案。我们在正确的信号分类概率方面比较了竞争方案。我们说明了所提议的方案在监视出租车服务中的行程持续时间数据中的使用。提出了一些结论和未来的研究问题。