Abstract
State-switching models such as hidden Markov models or Markov-switching regression models are routinely applied to analyse sequences of observations that are driven by underlying non-observable states. Coupled state-switching models extend these approaches to address the case of multiple observation sequences whose underlying state variables interact. In this article, we provide an overview of the modelling techniques related to coupling in state-switching models, thereby forming a rich and flexible statistical framework particularly useful for modelling correlated time series. Simulation experiments demonstrate the relevance of being able to account for an asynchronous evolution as well as interactions between the underlying latent processes. The models are further illustrated using two case studies related to (a) interactions between a dolphin mother and her calf as inferred from movement data and (b) electronic health record data collected on 696 patients within an intensive care unit.
Original language | English (US) |
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Pages (from-to) | 264-285 |
Number of pages | 22 |
Journal | Statistical Modelling |
Volume | 21 |
Issue number | 3 |
DOIs | |
State | Published - Jun 2021 |
Externally published | Yes |
Keywords
- Markov-switching regression
- Time series
- animal movement
- disease progression
- hidden Markov model
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty