Dynamic detection of change points in long time series

NXA Chopin

doi:10.1007/s10463-006-0053-9

Dynamic detection of change points in long time series

NXA Chopin

School of Mathematics

Research output: Contribution to journal › Article (Academic Journal) › peer-review

35 Citations (Scopus)

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
Original language	English
Pages (from-to)	349 - 366
Number of pages	18
Journal	Annals of the Institute of Statistical Mathematics
Volume	59 (2)
DOIs	https://doi.org/10.1007/s10463-006-0053-9
Publication status	Published - Jun 2007

Bibliographical note

Publisher: Springer

Access to Document

10.1007/s10463-006-0053-9

http://www.springerlink.com/content/2311t07p55v34835/

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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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AB - 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.

U2 - 10.1007/s10463-006-0053-9

DO - 10.1007/s10463-006-0053-9

M3 - Article (Academic Journal)

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JO - Annals of the Institute of Statistical Mathematics

JF - Annals of the Institute of Statistical Mathematics

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