Maximum pseudolikelihood estimation for model-based clustering of time series data

Nguyen, Hien D., McLachlan, Geoffrey J., Orban, Pierre, Bellec, Pierre and Janke, Andrew L. (2017) Maximum pseudolikelihood estimation for model-based clustering of time series data. Neural Computation, 29 4: 990-1020. doi:10.1162/NECO_a_00938


Author Nguyen, Hien D.
McLachlan, Geoffrey J.
Orban, Pierre
Bellec, Pierre
Janke, Andrew L.
Title Maximum pseudolikelihood estimation for model-based clustering of time series data
Journal name Neural Computation   Check publisher's open access policy
ISSN 0899-7667
1530-888X
Publication date 2017-04-01
Year available 2017
Sub-type Article (original research)
DOI 10.1162/NECO_a_00938
Open Access Status Not yet assessed
Volume 29
Issue 4
Start page 990
End page 1020
Total pages 31
Place of publication Cambridge, MA, United States
Publisher M I T Press
Language eng
Subject 1201 Arts and Humanities (miscellaneous)
2805 Cognitive Neuroscience
Abstract Mixture of autoregressions (MoAR) models provide a model-based approach to the clustering of time series data. The maximum likelihood (ML) estimation of MoAR models requires evaluating products of large numbers of densities of normal random variables. In practical scenarios, these products converge to zero as the length of the time series increases, and thus the ML estimation of MoAR models becomes infeasible without the use of numerical tricks. We propose a maximum pseudolikelihood (MPL) estimation approach as an alternative to the use of numerical tricks. The MPL estimator is proved to be consistent and can be computed with an EM (expectation-maximization) algorithm. Simulations are used to assess the performance of the MPL estimator against that of the ML estimator in cases where the latter was able to be calculated. An application to the clustering of time series data arising from a resting state fMRI experiment is presented as a demonstration of the methodology.
Keyword Gene-Expression Profiles
Mixture
Likelihood
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Mathematics and Physics
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