Improving how we estimate regression coefficients in longitudinal data analysis is crucial. By comparing two methods, researchers found that using a regularized-covariance-function-based estimator is more accurate than the conventional high-dimensional covariance matrix estimator when there are many time points per subject. This means the new method can give better results in estimating errors in the data. The study also looked at how well these methods work with incomplete data, using both simulated and real data to show their findings.