Dynamic detection of change points in long time series

DOI10.1007/s10463-006-0053-9zbMath1332.62316OpenAlexW2113166878MaRDI QIDQ995801

Nicolas Chopin

Publication date: 10 September 2007

Published in: Annals of the Institute of Statistical Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10463-006-0053-9

zbMATH Keywords

Markov chain Monte Carlostate space modelssequential Monte Carloparticle filterGARCH modelschange point models

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Bayesian inference (62F15) Monte Carlo methods (65C05)

Related Items (13)

Asymmetric Volatility Models with Structural Breaks ⋮ A pruned recursive solution to the multiple change point problem ⋮ An exact approach to Bayesian sequential change point detection ⋮ Stability of Feynman-Kac formulae with path-dependent potentials ⋮ Scalable Bayesian Multiple Changepoint Detection via Auxiliary Uniformisation ⋮ A consistent on‐line Bayesian procedure for detecting change points ⋮ Dynamic changepoint detection in count time series: a particle filter approach ⋮ Real time detection of structural breaks in GARCH models ⋮ Off-Line Detection of Multiple Change Points by the Filtered Derivative withp-Value Method ⋮ On rapid change points under long memory ⋮ A Kalman particle filter for online parameter estimation with applications to affine models ⋮ On-line changepoint detection and parameter estimation with application to genomic data ⋮ Bayesian multiple changepoint detection for stochastic models in continuous time

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