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Detecting Changes in a Poisson Process Monitored at Unequal Discrete Time Intervals

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  • Marlo Brown

Abstract

We look at a Poisson process where the arrival rate changes at some unknown time point. We monitor this process only at certain time points. At each time point, we count the number of arrivals that happened in that time interval. In previous work, it was assumed that the time intervals were equal. Since work schedules may prevent employees to monitor the process in the evenings or on weekends, we relax this assumption to allow for monitoring at unequal time intervals. For a loss function consisting of the cost of late detection and a penalty for early stopping, we develop, using dynamic programming, the one and two steps look ahead Bayesian stopping rules. We then compare various observation schemes to determine the best model. We provide some numerical results to illustrate the effectiveness of the detection procedures.

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  • Marlo Brown, 2009. "Detecting Changes in a Poisson Process Monitored at Unequal Discrete Time Intervals," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 38(16-17), pages 2721-2732, October.
    • Handle: RePEc:taf:lstaxx:v:38:y:2009:i:16-17:p:2721-2732
    DOI: 10.1080/03610910902936257
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    Cited by:

    1. Ameur, Hachmi Ben & Han, Xuyuan & Liu, Zhenya & Peillex, Jonathan, 2022. "When did global warming start? A new baseline for carbon budgeting," Economic Modelling, Elsevier, vol. 116(C).

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