Comparative performance analysis of the Cumulative Sum chart and the Shiryaev‐Roberts procedure for detecting changes in autocorrelated data

Comparative performance analysis of the Cumulative Sum chart and the Shiryaev‐Roberts procedure for detecting changes in autocorrelated data

We consider the problem of quickest changepoint detection where the observations form a first‐order autoregressive (AR(1)) process driven by temporally independent standard white Gaussian noise. Subject to possible change are both the drift of the AR(1) process (μ) and its correlation coefficient (λ...

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Veröffentlicht in:Applied stochastic models in business and industry 2018-11, Vol.34 (6), p.922-948
Hauptverfasser: Polunchenko, Aleksey S., Raghavan, Vasanthan
Format: Artikel
Sprache:eng
Schlagworte:
Asymptotic properties
Autocorrelation
Autoregressive processes
Change detection
Computer simulation
Control charts
Correlation coefficients
CUSUM chart
CUSUM charts
False alarms
Gaussian process
Mathematical models
Parameters
Performance measurement
Random noise
sequential analysis
sequential changepoint detection
Shiryaev‐Roberts procedure
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Beschreibung
Zusammenfassung:We consider the problem of quickest changepoint detection where the observations form a first‐order autoregressive (AR(1)) process driven by temporally independent standard white Gaussian noise. Subject to possible change are both the drift of the AR(1) process (μ) and its correlation coefficient (λ), which are both known. The change is abrupt and persistent, and of known magnitude, with |λ|
ISSN:1524-1904
1526-4025
DOI:10.1002/asmb.2372