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…
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2 Citations
Functional additive regression on shape and form manifolds of planar curves
- Computer Science
- 2021
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
- Computer Science
- 2021
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.
References
SHOWING 1-10 OF 42 REFERENCES
Modeling Motor Learning Using Heteroscedastic Functional Principal Components Analysis
- PsychologyJournal of the American Statistical Association
- 2018
A novel method for estimating population-level and subject-specific effects of covariates on the variability of functional data is proposed, motivated by a novel dataset from an experiment assessing upper extremity motor control, and quantifies the reduction in movement variability associated with skill learning.
Functional linear mixed models for irregularly or sparsely sampled data
- Mathematics, Computer Science
- 2015
An estimation approach to analyse correlated functional data, which are observed on unequal grids or even sparsely, based on dimension reduction via functional principal component analysis and on mixed model methodology is proposed.
Überblick über die Sportart Snooker, Entwicklung eines Muskeltrainings und Untersuchung dessen Einflusses auf die Stoßtechnik
- Unpublished Zulassungsarbeit, Technische Universität München.
- 2014
Überblicküber die Sportart Snooker, Entwicklung eines Muskeltrainings und Untersuchung dessen Einflusses auf die Stoßtechnik
- 2014
Multivariate Functional Principal Component Analysis for Data Observed on Different (Dimensional) Domains
- Mathematics
- 2015
ABSTRACT Existing approaches for multivariate functional principal component analysis are restricted to data on the same one-dimensional interval. The presented approach focuses on multivariate…
Functional Additive Mixed Models
- Computer ScienceJournal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America
- 2015
We propose an extensive framework for additive regression models for correlated functional responses, allowing for multiple partially nested or crossed functional random effects with flexible…
Elastic analysis of irregularly or sparsely sampled curves.
- Computer ScienceBiometrics
- 2022
Using spline curves to model smooth or polygonal means of open or closed curves with respect to the elastic distance and show identifiability of the spline model modulo parametrization is proposed.
Cross‐component registration for multivariate functional data, with application to growth curves
- Computer ScienceBiometrics
- 2020
A novel model for multivariate functional data where the component processes are subject to mutual time warping and the proposed methodology is based on a shift‐warping model, which differs from all existing registration methods for functional data in a fundamental way.
Graph-Fused Multivariate Regression via Total Variation Regularization.
- Computer Science, Mathematics
- 2020
The Graph-Fused Multivariate Regression via Total Variation regularization, a novel method for estimating the association between a one-dimensional or multidimensional array outcome and scalar predictors, is proposed.