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Activity Number: 26 - Imaging Speed Session
Type: Contributed
Date/Time: Sunday, August 8, 2021 : 1:30 PM to 3:20 PM
Sponsor: Section on Statistics in Imaging
Abstract #318990
Title: Markov-Switching State-Space Models with Applications to Neuroimaging
Author(s): David Degras* and Hernando Ombao and Chee Ming Ting
Companies: University of Massachusetts Boston and King Abdullah University of Science and Technology and Monash University Malaysia
Keywords: State-space model; Switching model; EM algorithm; Bootstrap; Dynamic Functional Connectivity; EEG

State-space models (SSM) with Markov switching offer a powerful framework for identifying multiple regimes in time series, analyzing interdependence and dynamics within regimes, and characterizing transitions between regimes. Markov-switching models present however great computational challenges due to the exponential number of possible regime sequences to account for. This paper proposes novel statistical methods for Markov-switching SSMs using maximum likelihood estimation, the EM algorithm, and bootstrapping. Considering three switching models, we develop solutions for initializing the EM, handling constraints on model parameters, accelerating convergence, and inferring parameters via parametric bootstrap. We assess the proposed methods by simulation and present applications to EEG studies of epilepsy and of motor imagery. All proposed methods are implemented as MATLAB functions available at

Authors who are presenting talks have a * after their name.

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