Dynamic detection of change points in long time series

NXA Chopin

Research output: Contribution to journalArticle (Academic Journal)peer-review

44 Citations (Scopus)


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 contributionDynamic detection of change points in long time series
Original languageEnglish
Pages (from-to)349 - 366
Number of pages18
JournalAnnals of the Institute of Statistical Mathematics
Volume59 (2)
Publication statusPublished - Jun 2007

Bibliographical note

Publisher: Springer


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