Detecting and modeling changes in a time series of proportions

DOI10.1214/21-AOAS1509zbMath1498.62167OpenAlexW4220943863MaRDI QIDQ2135370

Thomas J. Fisher, Stephen P. Colegate, Michael J. Vanni, Jing Zhang

Publication date: 6 May 2022

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1214/21-aoas1509

zbMATH Keywords

time serieshidden Markov modelchange pointscompositional dataDirichlet regression

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to environmental and related topics (62P12) Bayesian inference (62F15) Markov processes: estimation; hidden Markov models (62M05)

Related Items

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Uses Software

• depmixS4
• RStan
• npcp
• rstan
• loo
• ecp

Cites Work

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• Erratum to: ``Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC
• Inference in hidden Markov models.
• Structural breaks in time series
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• A Bayesian Analysis for Change Point Problems
• Hierarchical Bayesian Analysis of Changepoint Problems
• Bayesian Retrospective Multiple-Changepoint Identification
• A Nonparametric Approach for Multiple Change Point Analysis of Multivariate Data
• On-Line Inference for Multiple Changepoint Problems
• Changepoints in the North Atlantic Tropical Cyclone Record
• Mean shift testing in correlated data
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