A simple variable selection technique for nonlinear models
Applying nonparametric variable selection criteria in nonlinear regression models generally requires a substantial computational effort if the data set is large. In this paper we present a selection technique that is computationally much less demanding and performs well in comparison with methods currently available. It is based on a Taylor expansion of the nonlinear model around a given point in the sample space. Performing the selection only requires repeated least squares estimation of models that are linear in parameters. The main limitation of the method is that the number of variables among which to select cannot be very large if the sample is small and the order of an adequate Taylor expansion is high. Large samples can be handled without problems.
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|Date of creation:||03 Feb 1999|
|Date of revision:||06 Apr 2000|
|Publication status:||Published in Communications in Statistics, Theory and Methods, 2001, pages 1227-1241.|
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- Tschernig, Rolf & Yang, Lijian, 1997. "Nonparametric lag selection for time series," SFB 373 Discussion Papers 1997,59, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
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