A pairwise likelihood-based approach for changepoint detection in multivariate time series models

DOI10.1093/biomet/asw002zbMath1499.62314OpenAlexW2297691986WikidataQ39697771 ScholiaQ39697771MaRDI QIDQ5384380

Chun Yip Yau, Ting Fung Ma

Publication date: 24 June 2019

Published in: Biometrika (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1093/biomet/asw002

zbMATH Keywords

dynamic programmingstructural breakcomposite likelihoodmultiple changepoints

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Point estimation (62F10) Probability distributions: general theory (60E05) Non-Markovian processes: hypothesis testing (62M07) Exchangeability for stochastic processes (60G09)

Related Items (5)

Piecewise autoregression for general integer-valued time series ⋮ Change-point methods for multivariate time-series: paired vectorial observations ⋮ Most recent changepoint detection in censored panel data ⋮ Changepoint Detection in the Presence of Outliers ⋮ Multiple changepoint detection with partial information on changepoint times

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