Partial Correlation Graphs and Dynamic Latent Variables for Physiological Time Series
Latent variable techniques are helpful to reduce high-dimensional time series to a few relevant variables that are easier to model and analyze. An inherent problem is the identifiability of the model and the interpretation of the latent variables. We apply graphical models to find the essential relations in the data and to deduce suitable assumptions leading to meaningful latent variables.
KeywordsLatent Variable Variable Selection Multivariate Time Series Dynamic Factor Model Group Factor Analysis
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