Dynamic Detection of Change Points in Long Time Series |
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Authors: | Nicolas Chopin |
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Institution: | (1) School of Mathematics, University of Bristol, University Walk, Bristol, BS8 1TW, UK |
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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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Keywords: | Change point models GARCH models Markov chain Monte Carlo Particle filter Sequential Monte Carlo State state models |
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