Our data have 90% of people observed once and 10% of people at >= 2 time points. Our model includes person “random” effects modeled as
normal(0, sigma). Our dataset is huge, so even with only 10% repeated folks, we can estimate
sigma. Are there modeling / computational recommendations for this scenario to improve / speed up model fitting?
- K-fold CV in linear regression model with varying intercepts and very small clusters