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

Abstract

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 (C) 2009 Elsevier Ireland Ltd All rights reserved

Keywords

Growth curve models; Curve fitting; Noise reduction; Singular spectrum analysis

Published in

Computer Methods and Programs in Biomedicine
2009, volume: 96, number: 3, pages: 173-181

SLU Authors

• von Rosen, Dietrich

  • Department of Energy and Technology, Swedish University of Agricultural Sciences
    

UKÄ Subject classification

Computer Science

Publication identifier

• DOI: https://doi.org/10.1016/j.cmpb.2009.04.014

Permanent link to this page (URI)

https://res.slu.se/id/publ/29044