The researchers studied a way to estimate values in a type of data analysis called semiparametric regression for longitudinal data. They used least squares and local linear fitting methods to calculate these estimates. They found that the estimators for certain parts of the data have a normal pattern as the amount of data increases, and the estimate for another part improves at the best possible rate.