2 citations · 4 across the 5 of their papers we have counts for
4 papers · 1 filter
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…
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…
Prediction Inference Using Generalized Functional Mixed Effects Models
Xinkai Zhou, Erjia Cui, Joseph Sartini +1
We introduce inferential methods for prediction based on functional random effects in generalized functional mixed effects models. This is similar to the inference for random effec…
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…