3 papers
stat.ME2026
Bayesian Multivariate Sparse Functional Principal Components Analysis
Joseph Sartini, Scott Zeger, Ciprian Crainiceanu
Functional Principal Components Analysis (FPCA) provides a parsimonious, semi-parametric model for multivariate, sparsely-observed functional data. Frequentist FPCA approaches esti…
stat.ME2026
Sufficient conditions for proper posteriors in fully-Bayesian Functional PCA
Joseph Sartini, Scott Zeger, Ciprian Crainiceanu
In a fully-Bayesian Functional Principal Components Analysis (FPCA) the principal components are treated as unknown infinite-dimensional parameters. By projecting the functional pr…
stat.ME2025
Fast Bayesian Functional Principal Components Analysis
Joseph Sartini, Xinkai Zhou, Liz Selvin +2
Functional Principal Components Analysis (FPCA) is a widely used analytic tool for dimension reduction of functional data. Traditional implementations of FPCA estimate the principa…