Abstract
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 |
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| Original language | English |
| Pages (from-to) | 349 - 366 |
| Number of pages | 18 |
| Journal | Annals of the Institute of Statistical Mathematics |
| Volume | 59 (2) |
| DOIs | |
| Publication status | Published - Jun 2007 |