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

Hassani, Hossein, Zokaei, Mohammad, von Rosen, Dietrich, Amiri, Saeid and Ghodsi, Mansoureh 2009. Does noise reduction matter for curve fitting in growth curve models? Computer Methods and Programs in Biomedicine 96 (3) , pp. 173-181. 10.1016/j.cmpb.2009.04.014

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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.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Schools > Mathematics
Subjects:	Q Science > QA Mathematics
Uncontrolled Keywords:	growth curve models, curve fitting, noise reduction, singular spectrum analysis
Publisher:	Elsevier
ISSN:	0169-2607
Last Modified:	10 Oct 2017 14:06
URI:	https://orca.cardiff.ac.uk/id/eprint/24403

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