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.
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Chopin, N. Dynamic Detection of Change Points in Long Time Series. AISM 59, 349–366 (2007). https://doi.org/10.1007/s10463-006-0053-9
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DOI: https://doi.org/10.1007/s10463-006-0053-9