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B0867
Title: Functional additive regression for ordinal responses: Modeling EEG- based brain arousal state dynamics Authors:  Fabian Scheipl - Ludwig-Maximilians-Universitaet Muenchen (Germany) [presenting]
Juliane Minkwitz - Max Planck Institute of Psychiatry (Germany)
Abstract: The aim is to present an extension of penalized likelihood-based and boosting-based generalized additive models for functional responses to ordinal functional responses, i.e., multiple time series of ordinal measurements, and to show how to embed this problem in the general framework of functional regression models. The models are applied to high-frequency ordinal time series of vigilance levels derived from resting state EEG recordings in order to quantify potential associations between depressive symptoms and the temporal dynamics of brain arousal regulation.