2 citations · 2 across the 3 of their papers we have counts for
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Dynamic Prediction of High-density Generalized Functional Data with Fast Generalized Functional Principal Component Analysis
Ying Jin, Andrew Leroux
Dynamic prediction, which typically refers to the prediction of future outcomes using historical records, is often of interest in biomedical research. For datasets with large sampl…
Generalized Conditional Functional Principal Component Analysis
Yu Lu, Xinkai Zhou, Erjia Cui +4
We propose generalized conditional functional principal components analysis (GC-FPCA) for the joint modeling of the fixed and random effects of non-Gaussian functional outcomes. Th…
Comparing estimators of discriminative performance of time-to-event models
Ying Jin, Andrew Leroux
Predicting the timing and occurrence of events is a major focus of data science applications, especially in the context of biomedical research. Performance for models estimating th…
Fast Generalized Functional Principal Components Analysis
Andrew Leroux, Ciprian Crainiceanu, Julia Wrobel
We propose a new fast generalized functional principal components analysis (fast-GFPCA) algorithm for dimension reduction of non-Gaussian functional data. The method consists of: (…
Empirical Likelihood Inference of Variance Components in Linear Mixed-Effects Models
J. Zhang, W. Guo, J. S. Carpenter +6
Linear mixed-effects models are widely used in analyzing repeated measures data, including clustered and longitudinal data, where inferences of both fixed effects and variance comp…