Regime switching state-space models applied to psychological processes: handling missing data and making inferences

DOI10.1007/S11336-012-9254-8zbMath1284.62711OpenAlexW2130894835MaRDI QIDQ418428

Raoul P. P. P. Grasman, Ellen L. Hamaker

Publication date: 29 May 2012

Published in: Psychometrika (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11336-012-9254-8

zbMATH Keywords

regime switchingKalman filtermissing datastate-space model

Mathematics Subject Classification ID

Applications of statistics to psychology (62P15)

Related Items (4)

Forecasting intra-individual changes of affective states taking into account inter-individual differences using intensive longitudinal data from a university student dropout study in math ⋮ Nonlinear regime-switching state-space (RSSS) models ⋮ Bayesian dynamic modelling to assess differential treatment effects on panic attack frequencies ⋮ Pursuing collective synchrony in teams: a regime-switching dynamic factor model of speed similarity in soccer

Uses Software

• LBFGS-B
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Cites Work

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• Using state-space model with regime switching to represent the dynamics of facial electromyography (EMG) data
• A hierarchical Ornstein-Uhlenbeck model for continuous repeated measurement data
• Dynamic linear models with Markov-switching
• Posterior predictive checks: principles and discussion procedures
• Measurement invariance, factor analysis and factorial invariance
• Finite mixture and Markov switching models.
• A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle
• Model Selection and Multimodel Inference
• A Limited Memory Algorithm for Bound Constrained Optimization
• Maximum Likelihood Estimation of Misspecified Models

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