Statistical Modeling by GIC

Part of the Springer Series in Statistics book series (SSS)

The current wide availability of fast and inexpensive computers enables us to construct various types of nonlinear models for analyzing data having a complex structure. Crucial issues associated with nonlinear modeling are the choice of adjusted parameters including the smoothing parameter, the number of basis functions in splines and B-splines, and the number of hidden units in neural networks. Selection of these parameters in the modeling process can be viewed as a model selection and evaluation problem. This chapter addresses these issues as a model selection and evaluation problem and provides criteria for evaluating various types of statistical models.


Basis Function Smoothing Parameter Linear Logistic Model Nonlinear Regression Model Basis Expansion 
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Copyright information

© Springer Science+Business Media, LLC 2008

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