ArticleJournal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America2024
Functional Mixed Membership Models.
Article in Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Bayesian Causal Methods for Environmental Accountability Studies with Heterogeneous Effects.Current environmental health reports · 2026Review
- Bayesian covariance regression in functional data analysis with applications to functional brain imaging.The international journal of biostatistics · 2025Article
- Covariate Adjusted Functional Mixed Membership Models.Statistics and data science in imaging · 2025Article
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Authors and funding
6 authors.
Funding
Abstract
Mixed membership models, or partial membership models, are a flexible unsupervised learning method that allows each observation to belong to multiple clusters. In this paper, we propose a Bayesian mixed membership model for functional data. By using the multivariate Karhunen-Loève theorem, we are able to derive a scalable representation of Gaussian processes that maintains data-driven learning of the covariance structure. Within this framework, we establish conditional posterior consistency given a known feature allocation matrix. Compared to previous work on mixed membership models, our proposal allows for increased modeling flexibility, with the benefit of a directly interpretable mean and covariance structure. Our work is motivated by studies in functional brain imaging through electroencephalography (EEG) of children with autism spectrum disorder (ASD). In this context, our work formalizes the clinical notion of "spectrum" in terms of feature membership proportions. Supplementary materials, including proofs, are available online. The R package BayesFMMM is available to fit functional mixed membership models.
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