We consider the problem of detecting change points (structural changes) in long sequences of data, whether in a sequential fashion or not, and without assuming prior knowledge of the number of these change points. We reformulate this problem as the Bayesian filtering and smoothing of a non standard state space model. Towards this goal, we build a hybrid algorithm that relies on particle filtering and Markov chain Monte Carlo ideas. The approach is illustrated by a GARCH change point model.
|Translated title of the contribution
|Dynamic detection of change points in long time series
|349 - 366
|Number of pages
|Annals of the Institute of Statistical Mathematics
|Published - Jun 2007