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Nonparametric Estimation of the Human Height Growth Curve

  • Hans-Georg Müller
Part of the Lecture Notes in Statistics book series (LNS, volume 46)

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.

Keywords

Growth Curve Growth Velocity Kernel Method Kernel Estimate Adult Height 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1988

Authors and Affiliations

  • Hans-Georg Müller
    • 1
    • 2
  1. 1.Institute of Medical StatisticsUniversity of Erlangen-NürnbergErlangenFederal Republic of Germany
  2. 2.Division of StatisticsUniversity of CaliforniaDavisUSA

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