Abstract
As an example of an application of some of the methods discussed before, the analysis of the human height growth curve by nonparametric regression methods is considered. The data that are analysed were obtained in the Zurich Longitudinal Growth Study (1955–78) which was discussed already in 2.3. The nonparametric analysis of these data is published in Largo et al (1978) and Gasser et al (1984a,b; 1985a,b), and this chapter is based on the results of the latter four papers which are summarized and discussed. Of special interest for growth curves is the estimation of derivatives. Further, the comparison between parametric and nonparametric models, between smoothing splines and kernel estimators, the definition of longitudinal parameters and the phenomenon of growth spurts are discussed.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 1988 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Müller, HG. (1988). Nonparametric Estimation of the Human Height Growth Curve. In: Nonparametric Regression Analysis of Longitudinal Data. Lecture Notes in Statistics, vol 46. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-3926-0_9
Download citation
DOI: https://doi.org/10.1007/978-1-4612-3926-0_9
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-96844-5
Online ISBN: 978-1-4612-3926-0
eBook Packages: Springer Book Archive