SFB 303 Discussion Paper No. A - 368
Author: Gasser, Theo, and Alois Kneip
Title: Searching for Structure in Curve Samples
Abstract: Increasingly often, data arise in form of samples of curves. Examples
are physiological parameters measured over time for one or more samples of
subjects, or measurements obtained longitudinally for various experimental
units. In such a case it is important to know at an early stage of data
analysis which features are occuring consistently in the sample(s) of curves.
Such a definition is usually not easy due to substantial interindividual
variation both in x- and y-axis, and due to the influence of noise. A
method is proposed for defining features in a semi-automatic, nonparametric
way in a sample of curves. The consistent occurence of some feature in a
subinterval is extracted via the mode in a kernel estimated density of the
estimated features. Features may be extrema, inflection points etc.
("structural points"). They are obtained via kernel estimators designed
for estimating regression functions and their derivatives. Apart from a
theoretical foundation, the usefullness method is documented by application
to two interesting biomedical areas, i.e. research on growth and development
and neurophysiology.
Keywords: Curves, Regression, Feature extraction, extrema, density, estimation
JEL-Classification-Number:
Creation-Date: May 1992
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