3 papers
stat.CO2026
Learning Latent Memory States from Longitudinal Athlete Monitoring Data
Dae-Jin Lee
We propose a new unit of analysis for longitudinal data: the Latent Memory Table. The scientific contribution is not the encoder. It is that table, treated as a reusable statistica…
stat.ME2026
Stochastic EM Estimation and Inference for Zero-Inflated Beta-Binomial Mixed Models for Longitudinal Count Data
John Barrera, Ana Arribas-Gil, Dae-Jin Lee +1
Analyzing overdispersed, zero-inflated, longitudinal count data poses significant modeling and computational challenges, which standard count models (e.g., Poisson or negative bino…
stat.ML2025
Deep-SITAR: A SITAR-Based Deep Learning Framework for Growth Curve Modeling via Autoencoders
MarÃa Alejandra Hernández, Oscar Rodriguez, Dae-Jin Lee
Several approaches have been developed to capture the complexity and nonlinearity of human growth. One widely used is the Super Imposition by Translation and Rotation (SITAR) model…