检测持续时间未知的间歇性变化

IF 0.6 4区 数学 Q4 STATISTICS & PROBABILITY
Grigory Sokolov, V. Spivak, A. Tartakovsky
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引用次数: 1

摘要

摘要通常,在实践中,观察到的过程在未知的时间点改变统计特性,并且改变的持续时间基本上是有限的,在这种情况下,可以说改变是间歇性的或瞬态的。我们概述了间歇性变化检测的现有方法,并支持由变化的间歇性驱动的特定设置。我们提出了一种新的优化标准,该标准更适用于许多应用领域,如物理计算机系统中的威胁检测、近地空间信息学、流行病学、药代动力学等,与需要最小化检测的预期延迟的传统最快变化检测方法相比,最大化检测的局部条件概率是更合理的方法。我们采用了关于变化持续时间的最大似然(ML)方法,并表明几种常用的检测规则(累积和[CUSUM]、窗口受限[WL]-CUSUM和有限移动平均[FMA])等效于基于ML的停止时间。我们讨论了如何为这些规则选择设计参数,并提供了一个全面的模拟研究来证实直观的预期。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Detecting an intermittent change of unknown duration
Abstract Oftentimes, in practice, the observed process changes statistical properties at an unknown point in time and the duration of a change is substantially finite, in which case one says that the change is intermittent or transient. We provide an overview of existing approaches for intermittent change detection and advocate in favor of a particular setting driven by the intermittent nature of the change. We propose a novel optimization criterion that is more appropriate for many applied areas such as the detection of threats in physical computer systems, near-Earth space informatics, epidemiology, pharmacokinetics, etc. We argue that controlling the local conditional probability of a false alarm, rather than the familiar average run length to a false alarm, and maximizing the local conditional probability of detection is a more reasonable approach versus a traditional quickest change detection approach that requires minimizing the expected delay to detection. We adopt the maximum likelihood (ML) approach with respect to the change duration and show that several commonly used detection rules (cumulative sum [CUSUM], window-limited [WL]-CUSUM, and finite moving average [FMA]) are equivalent to the ML-based stopping times. We discuss how to choose design parameters for these rules and provide a comprehensive simulation study to corroborate intuitive expectations.
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来源期刊
CiteScore
1.40
自引率
12.50%
发文量
20
期刊介绍: The purpose of Sequential Analysis is to contribute to theoretical and applied aspects of sequential methodologies in all areas of statistical science. Published papers highlight the development of new and important sequential approaches. Interdisciplinary articles that emphasize the methodology of practical value to applied researchers and statistical consultants are highly encouraged. Papers that cover contemporary areas of applications including animal abundance, bioequivalence, communication science, computer simulations, data mining, directional data, disease mapping, environmental sampling, genome, imaging, microarrays, networking, parallel processing, pest management, sonar detection, spatial statistics, tracking, and engineering are deemed especially important. Of particular value are expository review articles that critically synthesize broad-based statistical issues. Papers on case-studies are also considered. All papers are refereed.
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