Journal of Computational and Graphical Statistics· 2026Q1
Gaussian Graphical Models for Partially Observed Multivariate Functional Data
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- Q1SCImago
- 2026year
Short summary
A novel EM-type algorithm enables penalized inference of functional graphical models from partially observed multivariate functional data, overcoming challenges posed by unobserved components.
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Key points
- Develops an EM-type algorithm for penalized inference of functional graphical models.
- Handles partially observed multivariate functional data, where functional principal component scores are not directly computable.
- Assumes Gaussianity and partial separability of the covariance operator.
- Validated through a simulation study and an illustration on ESG data.
AI-generated from the title and abstract; the full text is not read.
Abstract
In many applications, the variables that characterize a stochastic system are measured along a second dimension, such as time. This results in multivariate functional data and the interest is in describing the statistical dependencies among these variables. It is often the case that the functional data are only partially observed. This creates additional challenges to statistical inference, since the functional principal component scores, which capture all the information from these data, cannot be computed. Under an assumption of Gaussianity and of partial separability of the covariance operator, we develop an Expectation-Maximization (EM)-type algorithm for penalized inference of a functional graphical model from multivariate functional data which are only partially observed. A simulation study and an illustration on environmental, social and governance (ESG) data show the potential of the proposed method.
The authors' abstract, as published at the source. Journal of Computational and Graphical Statistics, 2026 · DOI ↗
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Field: Statistics and Probability
Statistics and ProbabilityMathematics