Multivariate Sparse Functional Data Classification via Bayesian Aggregation

Music City Center 

Multivariate functional data arise in a wide range of applications, from medical diagnostics to economic time series. However, classification becomes notably difficult when data are sparsely and irregularly observed. To address this challenge, we propose a novel Bayesian ensemble framework that integrates multivariate functional principal component analysis (MFPCA) with probabilistic aggregation. Our method first extracts key features from the multivariate functional observations using MFPCA, then generates multiple bootstrap samples to capture variability in the data. Rather than relying on conventional ensemble heuristics, the proposed approach employs Bayesian generalized linear models (Bayesian GLMs) to systematically calibrate and combine predicted probabilities across bootstrap iterations. This principled treatment of uncertainty leads to more accurate and reliable classification outcomes. Extensive simulations and real-world case studies demonstrate that our framework consistently outperforms standard single classifiers and traditional ensemble techniques.

Multivariate Functional Principal Component Analysis (MFPCA)

Sparse Longitudinal Data


Functional Principal Component Analysis (FPCA)

Bootstrap Aggregating

Classification

Statistical learning 

Section on Statistical Learning and Data Science