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

Sammanfattning

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

Nyckelord

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

Publicerad i

Computer Methods and Programs in Biomedicine
2009, volym: 96, nummer: 3, sidor: 173-181

SLU författare

• von Rosen, Dietrich

  • Institutionen för energi och teknik, Sveriges lantbruksuniversitet
    

UKÄ forskningsämne

Datavetenskap (datalogi)

Publikationens identifierare

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

Permanent länk till denna sida (URI)

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