Does noise reduction matter for curve fitting in growth curve models?

In this paper, we discuss the efficiency of noise reduction for curve fitting in nonlinear growth curve models. We use singular spectrum analysis as a nonlinear–nonparametric denoising method. A set of longitudinal measurements is used in considering the performance of the method. We also use artificially generated data sets with and without noise for the purpose of validation of the results obtained in this study. The results show that noise reduction is important for curve fitting in growth curve models and also, that the singular spectrum analysis technique can be used as a powerful tool for noise reduction in longitudinal measurements.