Changepoint detection in multivariate poisson distributions

The paper addresses the problem of detecting changes in the parameters of multivariate Poisson sequences. The Neyman Pearson Detector (NPD) and the Generalized Likelihood Ratio Detector (GLRD) are easily derived for iid Poisson sequences. Unfortunately, the problem is more complicated for correlated Poisson sequences. This paper studies the performance of the detectors obtained under the iid assumption, when the observed data are distributed according to multivariate correlated Poisson sequences. Theoretical expressions of the receiver operating characteristics are derived when the change location is known. These curves provide a reference to which other detectors can be compared. The more realistic situation of an unknown change location is finally considered.