Representing time-varying cyclic dynamics usingmultiple-subject state-space models

摘要

Over the last few decades, researchers have become increasingly aware of the need toconsider intraindividual variability in the form of cyclic processes. In this paper, wereview two contemporary cyclic state-space models: Young and colleagues' dynamicharmonic regression model and Harvey and colleagues' stochastic cycle model. Wefurther derive the analytic equivalence between the two models, discuss their uniquestrengths and propose multiple-subject extensions. Using data from a study on humanpostural dynamics and a daily affect study, we demonstrate the use of these modelsto represent within-person non-stationarities in cyclic dynamics and interindividualdifferences therein. The use of diagnostic tools for evaluating model fit is alsoillustrated.