6 papers · 1 filter
PCA score regression: the art of losing power
Yu Lu, Nidhi Pai, Erjia Cui +1
The regression of principal component scores (RPCS) on covariates is a widely used analytic approach to detect and test for associations between functional measurements and study p…
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…
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…
Functional Moments Regression
Mingyuan Li, Martin A. Lindquist, Edward Gunning +1
The Gaussian Process (GP) assumption is often used in functional data analysis. We propose a method to assess departures from the GP assumption, both in terms of the shape of the d…
Tutorial on Bayesian Functional Regression Using Stan
Ziren Jiang, Ciprian Crainiceanu, Erjia Cui
This manuscript provides step-by-step instructions for implementing Bayesian functional regression models using Stan. Extensive simulations indicate that the inferential performanc…
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…