Hanchang Cai

The paper evaluates clustering methods for multivariate longitudinal data using a real-world eCDF-Copula resampling framework to address missing data and improve benchmarking.

This paper introduces eCDF-copula, a method for generating synthetic longitudinal tabular data that preserves within- and between-visit dependence structures, outperforming existing methods in resemblance and utility.

The paper introduces a mixed-effects location-scale model to analyze variability in longitudinal actigraphy data, showing that modeling both mean and variability improves understanding of sleep patterns in schizophrenia.