Detection of change in periodic, nonstationary data

Traditional change detection strategies are limited in cases where the data are nonstationary and the distributions are unknown. We present an algorithm for change detection problems in which we do not know the form of the distribution. Our algorithm uses distributed detection with a bank of type-based front end detectors that achieve asymptotically optimal type I error performance. Our simulations indicate that this algorithm performs much better than traditional methods