Multivariate functional additive mixed models

@article{Volkmann2021MultivariateFA,
  title={Multivariate functional additive mixed models},
  author={Alexander Volkmann and Almond Stocker and Fabian Scheipl and Sonja Greven},
  journal={Statistical Modelling},
  year={2021}
}
Multivariate functional data can be intrinsically multivariate like movement trajectories in 2D or complementary such as precipitation, temperature and wind speeds over time at a given weather station. We propose a multivariate functional additive mixed model (multiFAMM) and show its application to both data situations using examples from sports science (movement trajectories of snooker players) and phonetic science (acoustic signals and articulation of consonants). The approach includes linear… 
Functional additive regression on shape and form manifolds of planar curves
TLDR
A Riemannian L2-Boosting algorithm well-suited for a potentially large number of possibly parameter-intensive model terms is proposed, which also yields automated model selection and novel intuitively interpretable visualizations for (even non-linear) covariate effects in the shape/form space via suitable tensor-product factorization.
Functional additive models on manifolds of planar shapes and forms
TLDR
This work proposes a Riemannian L2-Boosting algorithm well suited for a potentially large number of possibly parameter-intensive model terms, which also yields automated model selection and provides novel intuitively interpretable visualizations for covariate effects in the shape/form space via suitable tensor-product factorization.

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