Detecting changes in a Poisson process monitored at random time intervals. Issue 3 (2nd July 2016) - Brown, Marlo
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 fixed in advance. We relax this assumption to assume that the time intervals in which the process are monitored is also random. 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-step 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.