Splus Tools for Model Selection in Nonlinear Regression
Please always quote using this URN: urn:nbn:de:0297-zib-2104
- The results of analyzing experimental data using a parametric model may heavily depend on the chosen model. With this paper we describe computational tools in Splus for the adequate selection of nonlinear regression models if the intended use of the model is among the following: 1. estimation of the unknown regression function, 2. prediction of future values of the response variable, 3. calibration or 4. estimation of some parameter with a certain meaning in the corresponding field of application. Moreover, we provide programs for variance modelling and for selecting an appropriate nonlinear transformation of the observations which may lead to an improved accuracy. We describe how the accuracy of the parameter estimators is assessed by a "moment oriented bootstrap procedure". This procedure is also used for the construction of confidence, prediction and calibration intervals. The use of our tools is illustrated by an example. Help files are given in an appendix.
Author: | Olaf Bunke, Bernd Droge, Jörg Polzehl |
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Document Type: | ZIB-Report |
Date of first Publication: | 1995/12/19 |
Series (Serial Number): | ZIB-Report (SC-95-44) |
ZIB-Reportnumber: | SC-95-44 |
Published in: | Appeared in: Computational Statistics 12 (1998) 257-281 |