Nonparametric linear mixed effects models are used to handle data without strict assumptions. Smoothing techniques are crucial for these models, especially when there are measurement errors. The researchers in this paper introduced a wavelet method to smooth out nonparametric functions in these models, even with known measurement errors. They found that their proposed model performed much better in predicting random effects compared to models that ignored measurement errors.