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|
|Pages (from-to)||349 - 366|
|Number of pages||18|
|Journal||Annals of the Institute of Statistical Mathematics|
|Publication status||Published - Jun 2007|