Some applications of nonlinear and non-Gaussian state–space modelling by means of hidden Markov models

DOI10.1080/02664763.2011.573543OpenAlexW1964491534MaRDI QIDQ5124974

Roland Langrock

Publication date: 30 September 2020

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

Full work available at URL: https://doi.org/10.1080/02664763.2011.573543

zbMATH Keywords

numerical integrationtime seriesstochastic volatilitycount dataViterbi algorithmbinary datapseudo-residuals

Mathematics Subject Classification ID

Statistics (62-XX)

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Uses Software

• SsfPack
• astsa

Cites Work

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• Hidden Markov models with arbitrary state dwell-time distributions
• Likelihood-based inference for asymmetric stochastic volatility models
• Markov chain Monte Carlo methods for stochastic volatility models.
• Inference in hidden Markov models.
• Poisson Autoregression
• Non-Gaussian State-Space Modeling of Nonstationary Time Series
• A regression model for time series of counts
• Monte Carlo maximum likelihood estimation for non-Gaussian state space models
• Hidden Markov processes
• Statistical algorithms for models in state space using SsfPack 2.2
• Monte Carlo EM Estimation for Time Series Models Involving Counts
• Remarks on a Multivariate Transformation
• Hidden Markov Models for Time Series