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Cited by 166Advanced Statistical Process MonitoringFault Detection and Control SystemsProbability and Statistical Research

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Performance of CUSUM Control Schemes for Serially Correlated Observations

Yashchin, Emmanuel·1993·Technometrics
DOI 10.2307/1269288
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Abstract

This article discusses situations in which one is interested in evaluating the run-length characteristics of a cumulative sum control scheme when the underlying data show the presence of serial correlation. In practical applications, situations of this type are common in problems associated with monitoring such characteristics of data as forecasting errors, measures of model adequacy, and variance components. The discussed problem is also relevant in situations in which data transformations are used to reduce the magnitude of serial correlation. The basic idea of analysis involves replacing the sequence of serially correlated observations by a sequence of independent and identically distributed observations for which the runlength characteristics of interest are roughly the same. Applications of the proposed method for several classes of processes arising in the area of statistical process control are discussed in detail, and it is shown that it leads to approximations that can be considered acceptable in many practical situations.

Overview

Integrity signals

Registry

Crossref · 10.1080/00401706.1993.10484992

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  • Only a handful of surveys about the explainability of anomaly detection methods exist.

    “In contrast, we only found a handful of surveys [162,189,225] about the explainability of anomaly detection methods.”

    From A Survey on Explainable Anomaly Detection (2024) · cites this paper as [225]

  • Existing surveys on explainable anomaly detection are either outdated, have missed important work, or have relatively coarse taxonomies.

    “However, after a thorough survey of academic publications on explainable anomaly detection, we found that existing surveys are either outdated, have missed some important work, or their proposed taxonomies are relatively coarse and, therefore, unable to characterize the increasingly rich set of explainable anomaly detection techniques available in the literature.”

    From A Survey on Explainable Anomaly Detection (2024) · cites this paper as [225]

  • Existing taxonomies of XAD are relatively coarse and sometimes not intuitive.

    “In addition, we find the existing taxonomies to be relatively coarse and sometimes not intuitive.”

    From A Survey on Explainable Anomaly Detection (2024) · cites this paper as [225]

Citations

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Cited by

Sorted by citations

  • A Survey on Explainable Anomaly Detection

    Li, Z., Zhu, Y., & Van Leeuwen, M. (2024) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3609333 · Cited by 139

    “On this basis, they extend the CUMSUM algorithm [225] to detect anomalies by using the likelihood ratio between two windows of prediction residuals.”
    Method

Metadata

Authors
Yashchin, E.
Affiliations
IBM Research - Thomas J. Watson Research Center (US)
Corresponding author
Yashchin, Emmanuel
ORCID
Yashchin, Emmanuel
Venue
Technometrics
Publisher
Informa UK Limited
Published
1993-02
Volume
35
Issue
1
Pages
37-52
ISSN
  • 0040-1706 (ISSN-L)
  • 1537-2723
DOI
10.2307/1269288
OpenAlex
W1974697235
Cited by
166
References
0
Citations by year
2026: 2 · 2025: 6 · 2024: 2 · 2023: 4 · 2022: 2 · 2021: 1 · 2020: 3 · 2019: 3 · 2018: 7 · 2017: 1 · 2016: 2 · 2015: 5 · 2014: 4 · 2013: 6 · 2012: 3
FWCI
15.01
Type
journal article
Language
en
Open access
closed
Locations
Technometrics (publishedVersion)
Topics
  • Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Advanced Statistical Process Monitoring
  • Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
  • Physical Sciences › Mathematics › Statistics and Probability › Probability and Statistical Research
Keywords
CUSUM control chart, serial correlation, run length characteristics, statistical process control, data transformation, forecast error, model adequacy, variance components

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