Non-restarting cumulative sum charts and control of the false discovery rate

Lau, F. Din-Houn

doi:10.1093/biomet/ass066

Cited by 30 publications

(16 citation statements)

References 17 publications

(26 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Woodall and Montgomery (2014) listed several papers on the use of the false discovery rate approach in process monitoring. In addition, see Gandy and Lau (2013). Some of the network monitoring methods, for example those by Heard et al (2010) and Vigliotti and Hankin (2015), are already p-value based with a concern over the high number of false positives so use of a false discovery rate approach seems promising.…”

Section: Research Opportunities and Conclusionmentioning

confidence: 99%

An overview and perspective on social network monitoring

et al. 2016

View full text Add to dashboard Cite

In this expository paper we give an overview of some statistical methods for the monitoring of social networks. We discuss the advantages and limitations of various methods as well as some relevant issues. One of our primary contributions is to give the relationships between network monitoring methods and monitoring methods in engineering statistics and public health surveillance. We encourage researchers in the industrial process monitoring area to work on developing and comparing the performance of social network monitoring methods. We also discuss some of the issues in social network monitoring and give a number of research ideas.

show abstract

Section: Research Opportunities and Conclusionmentioning

confidence: 99%

An overview and perspective on social network monitoring

et al. 2016

View full text Add to dashboard Cite

show abstract

“…Using the CUSUM statistic to determine the beginning and end of an attack, as in their Figure 4 (right), seems very close in goal to the method of Gandy and Lau . The level of attack illustrated in Figure 4 (left) is relatively high.…”

Section: Computer Network Surveillancementioning

confidence: 77%

Discussion of “Statistical methods for network surveillance”

Sengupta

Woodall

2018

Appl Stoch Models Bus & Ind

View full text Add to dashboard Cite

We appreciate the opportunity to discuss this interesting and thought-provoking paper. We agree with the authors that there is a growing interest in network surveillance with many important applications.In the context of network surveillance, we think it is important to distinguish between the case where the network is deterministic and the case where the network is random.The first case arises when the network itself (ie, the set of vertices/nodes and the set of edges/links) is considered to be fixed and deterministic, and some kind of traffic is being transmitted via the network links. The traffic flow being transmitted through each network link is considered to be a random observation from some statistical model, and the goal of network surveillance in this scenario is to detect whether the random traffic flow through the deterministic network has significantly deviated from normal or baseline conditions. Two of the three examples in the paper fall under this category, namely, monitoring network security and monitoring reliability of data networks. This general framework also includes a variety of interconnected systems beyond computer networks, for example, vehicular traffic on the interstate highway network or electric power transmission via the power grid network. The underlying principles of network surveillance from computer networks can therefore be extended to such networked systems.The second case arises when networks at different point of time are considered to be a time series of random observations from a statistical model, such as the degree corrected stochastic blockmodel in the authors' Section 5.3.2. In this case, the network structure itself is the observed sequence of random variables, and the goal of network surveillance is to detect whether the network structure has significantly deviated from normal or baseline conditions. Examples include email networks (ie, the Enron network from their Section 5), social media networks, and friendship networks.These two cases are inherently different in terms of the statistical framework and the objective of network surveillance. In the first case, the objective is to carry out surveillance on networks, whereas in the second case, the objective is to carry out surveillance of networks. The umbrella term network surveillance appears to include both cases, but we think it is better to distinguish between them, preferably by using different terms to describe the two cases. Perhaps, fixed network surveillance and random network surveillance would work.There are sampling issues with respect to any type of surveillance. For example, how often should the process be sampled? Over what time period should the data be aggregated? In the authors' Figure 2, the number of users is recorded at five-minute intervals. What was the justification for this frequency of sampling? Sampling at a high frequency can make modeling more difficult, whereas sampling at lower frequencies means that detection of anomalies may be unsuccessful or delayed. Zwetsloot and Woodall 1 discusse...

show abstract

“…As a result, this type of monitoring schemes needs to identify which data streams are OC. The monitoring schemes proposed in in Grigg and Spiegelhalter (2008), Tsung (2009, 2012), and Gandy and Lau (2013) belong to this category. In the past, the research on these two types of monitoring schemes has not overlapped.…”

Section: Discussionmentioning

confidence: 99%

“…The research problem of monitoring high-dimensional data streams for this purpose has attracted considerable attention in the literature recently. A number of monitoring schemes have been proposed, including those in Grigg and Spiegelhalter (2008), Tsung (2009, 2012), and Gandy and Lau (2013). A common theme in those methods is to monitor each data stream by one control chart, and calculate the p-values of the charting statistics, and then apply some existing false discover rate (FDR) controlling procedure from the multiple hypothesis testing literature to those p-values at each time point.…”

Section: Introductionmentioning

confidence: 99%

A two-stage online monitoring procedure for high-dimensional data streams

2018

Journal of Quality Technology

View full text Add to dashboard Cite

Advanced computing and data acquisition technologies have made possible the collection of high-dimensional data streams in many fields. Efficient online monitoring tools which can correctly identify any abnormal data stream for such data are highly sought after. However, most of the existing monitoring procedures directly apply the false discover rate (FDR) controlling procedure to the data at each time point, and the FDR at each time point (the point-wise FDR) is either specified by users or determined by the in-control (IC) average run length (ARL). If the point-wise FDR is specified by users, the resulting procedure lacks control of the global FDR and keeps users in the dark in terms of the IC-ARL. If the point-wise FDR is determined by the IC-ARL, the resulting procedure does not give users the flexibility to choose the number of false alarms (Type-I errors) they can tolerate when identifying abnormal data streams, which often makes the procedure too conservative. To address those limitations, we propose a twostage monitoring procedure that can control both the IC-ARL and Type-I errors at the levels specified by users. As a result, the proposed procedure allows users to choose not only how often they expect any false alarms when all data streams are IC, but also how many false alarms they can tolerate when identifying abnormal data streams.With this extra flexibility, our proposed two-stage monitoring procedure is shown in the simulation study and real data analysis to outperform the exiting methods.

show abstract

Non-restarting cumulative sum charts and control of the false discovery rate

Cited by 30 publications

References 17 publications

An overview and perspective on social network monitoring

An overview and perspective on social network monitoring

Discussion of “Statistical methods for network surveillance”

A two-stage online monitoring procedure for high-dimensional data streams

Contact Info

Product

Resources

About