Title: Switching principal component analysis for modeling means and covariance changes over time
Authors: De Roover, Kim ×
Timmerman, Marieke E.
Van Diest, Ilse
Onghena, Patrick
Ceulemans, Eva #
Issue Date: 2014
Publisher: American Psychological Association
Series Title: Psychological Methods vol:19 issue:1 pages:113-132
Abstract: Many psychological theories predict that cognitions, affect, action tendencies, and other variables change across time in mean level as well as in covariance structure. Often, such changes are rather abrupt, because they are caused by sudden events. To capture such changes, one may repeatedly measure the variables under study for a single individual, and examine whether the resulting multivariate time series contains a number of phases with different means and covariance structures. The latter task is challenging, however. First, in many cases, it is unknown how many phases there are, and when new phases start. Second, often a rather large number of variables is involved, complicating the interpretation of the covariance pattern within each phase. To take up this challenge, we present Switching PCA. Switching PCA detects phases of consecutive observations or time points (in single subject data) with similar means and/or covariation structures, and performs a principal component analysis (PCA) per phase to yield insight into its covariance structure. An algorithm for fitting Switching PCA solutions as well as a model selection procedure are presented and evaluated in a simulation study. Finally, we analyze empirical data on cardio-respiratory recordings.
ISSN: 1082-989X
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Methodology of Educational Sciences
Health Psychology
× corresponding author
# (joint) last author

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