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. 2023 Mar 13;50(14):2862-2888.
doi: 10.1080/02664763.2023.2174257. eCollection 2023.

Detection and estimation of multiple transient changes

Michael Baron  1 , Sergey V Malov  2   3   4
Affiliations

Detection and estimation of multiple transient changes

Michael Baron et al. J Appl Stat. .

Abstract

Change-point detection methods are proposed for the case of temporary failures, or transient changes, when an unexpected disorder is ultimately followed by a re-adjustment and return to the initial state. A base distribution of the 'in-control' state changes to an 'out-of-control' distribution for unknown periods of time. Likelihood based sequential and retrospective tools are proposed for the detection and estimation of each pair of change-points. The accuracy of the obtained change-point estimates is assessed. Proposed methods offer simultaneous control of the familywise false alarm and false re-adjustment rates at the pre-chosen levels.

Keywords: CUSUM process; Change-point problem; false alarm; maximum likelihood estimate; transient changes.

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Conflict of interest statement

No potential conflict of interest was reported by the author(s).

Figures

Figure 1.
Figure 1.
Spikes in instantaneous electricity prices during two years in the PJM (Pennsylvania–New Jersey–Maryland) energy market.
Figure 2.
Figure 2.
Maximum likelihood estimation of a single transient change interval. The likelihood-ratio test statistic Λ is the largest increment of both processes St and Wt.
Figure 3.
Figure 3.
Estimation of multiple change-points.
Figure 4.
Figure 4.
Operating characteristics. ROC curves for change detection in the mean with various thresholds.
Figure 5.
Figure 5.
Lower bound estimation of required thresholds.
See this image and copyright information in PMC

References

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