Multivariate functional data present theoretical and practical complications that are not found in univariate functional data. One of these is a situation where the component functions of multivariate functional data are positive and are subject to mutual time warping. That is, the component processes exhibit a common shape but are subject to systematic phase variation across their domains in addition to subject‐specific time warping, where each subject has its own internal clock. This motivates a novel model for multivariate functional data that connect such mutual time warping to a latent‐deformation‐based framework by exploiting a novel time‐warping separability assumption. This separability assumption allows for meaningful interpretation and dimension reduction. The resulting latent deformation model is shown to be well suited to represent commonly encountered functional vector data. The proposed approach combines a random amplitude factor for each component with population‐based registration across the components of a multivariate functional data vector and includes a latent population function, which corresponds to a common underlying trajectory. We propose estimators for all components of the model, enabling implementation of the proposed data‐based representation for multivariate functional data and downstream analyses such as Fréchet regression. Rates of convergence are established when curves are fully observed or observed with measurement error. The usefulness of the model, interpretations, and practical aspects are illustrated in simulations and with application to multivariate human growth curves and multivariate environmental pollution data.
Multivariate functional data are increasingly encountered in data analysis, whereas statistical models for such data are not well developed yet. Motivated by a case-study where one aims to quantify the relationship between various longitudinally recorded behaviour intensities for Drosophila flies, we propose a functional linear manifold model. This model reflects the functional dependence between the components of multivariate random processes and is defined through data-determined linear combinations of the multivariate component trajectories, which are characterized by a set of varying-coefficient functions. The time varying linear relationships that govern the components of multivariate random functions yield insights about the underlying processes and also lead to noise-reduced representations of the multivariate component trajectories. The functional linear manifold model proposed is put to the task for an analysis of longitudinally observed behavioural patterns of flying, feeding, walking and resting over the lifespan of Drosophila flies and is also investigated in simulations.
more » « less- PAR ID:
- 10401333
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Journal of the Royal Statistical Society Series B: Statistical Methodology
- Volume:
- 76
- Issue:
- 3
- ISSN:
- 1369-7412
- Format(s):
- Medium: X Size: p. 605-626
- Size(s):
- p. 605-626
- Sponsoring Org:
- National Science Foundation
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