Nonparametric regression as an example of model choice
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Date
2006-05-04T09:55:49Z
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Abstract
Nonparametric regression can be considered as a problem of model choice. In this paper
we present the results of a simulation study in which several nonparametric regression
techniques including wavelets and kernel methods are compared with respect to their
behaviour on different test beds. We also include the taut-string method whose aim is
not to minimize the distance of an estimator to some “true” generating function f but to
provide a simple adequate approximation to the data. Test beds are situations where a
“true” generating f exists and in this situation it is possible to compare the estimates of
f with f itself. The measures of performance we use are the L^2 and the L^infinity norms and
the ability to identify peaks.
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Keywords
Kernel method, Nonparametric regression, Simulation study, Taut string method, Wavelet