Dynamic Detection of Change Points in Long Time Series


DOI: 10.1007/s10463-006-0053-9

Cite this article as:
Chopin, N. AISM (2007) 59: 349. doi:10.1007/s10463-006-0053-9


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.


Change point models GARCH models Markov chain Monte Carlo Particle filter Sequential Monte Carlo State state models 

Copyright information

© The Institute of Statistical Mathematics, Tokyo 2006

Authors and Affiliations

  1. 1.School of MathematicsUniversity of BristolBristolUK

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