序列检测后常见变化点的估计和变化面板的隔离

IF 0.6 4区数学 Q4 STATISTICS & PROBABILITY

Sequential Analysis-Design Methods and Applications Pub Date : 2019-07-03 DOI:10.1080/07474946.2020.1726685

Yanhong Wu

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引用次数: 9

摘要

在多流数据的顺序监控中，快速检测共同变化是至关重要的，其中共同变化仅发生在部分面板中。在使用组合累积和Shiryaev-Roberts (CUSUM- sr)程序检测共同变化之后，我们首先研究了CUSUM过程值的联合分布和不变面板的估计延迟检测时间。在控制错误发现率(FDR)的情况下，利用CUSUM过程的渐近指数性质，提出了一种分离变化面板的Benjamini-Hochberg (BH)方法。然后根据隔离的更改面板估计公共更改点。仿真结果表明，通过合理选择FDR，该方法可以有效地控制错误未发现率(FNR)。

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Estimation of common change point and isolation of changed panels after sequential detection

Abstract Quick detection of common changes is critical in sequential monitoring of multistream data where a common change is a change that only occurs in a portion of panels. After a common change is detected by using a combined cumulative sum Shiryaev-Roberts (CUSUM-SR) procedure, we first study the joint distribution for values of the CUSUM process and the estimated delay detection time for the unchanged panels. A Benjamini-Hochberg (BH) method using the asymptotic exponential property for the CUSUM process is developed to isolate the changed panels with control on the false discovery rate (FDR). The common change point is then estimated based on the isolated changed panels. Simulation results show that the proposed method can also control the false non-discovery rate (FNR) by properly selecting the FDR.

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来源期刊

Sequential Analysis-Design Methods and Applications STATISTICS & PROBABILITY-

CiteScore

1.40

自引率

12.50%

发文量

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.