One-step approximations for detecting regime changes in the state space model with application to the influenza data

DOI10.1016/j.csda.2007.08.019zbMath1452.62699OpenAlexW2070464681MaRDI QIDQ1023561

Tianni Zhou, Robert H. Shumway

Publication date: 12 June 2009

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2007.08.019

zbMATH Keywords

EM algorithmhidden Markov modelMarkov chain Monte CarloKalman filterGibbs samplinginfluenzaKalman smootherstate space model with switching

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Inference from stochastic processes and prediction (62M20) Applications of statistics to biology and medical sciences; meta analysis (62P10) Markov processes: estimation; hidden Markov models (62M05)

Related Items

Markov-switching state-space models with applications to neuroimaging ⋮ Online pattern recognition based on a generalized hidden Markov model for intraoperative vital sign monitoring

Uses Software

• astsa

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• AN APPROACH TO TIME SERIES SMOOTHING AND FORECASTING USING THE EM ALGORITHM
• Dynamic linear models with Markov-switching
• Monitoring Renal Transplants: An Application of the Multiprocess Kalman Filter
• On Gibbs sampling for state space models

This page was built for publication: One-step approximations for detecting regime changes in the state space model with application to the influenza data