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.
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© 2008 Springer Science+Business Media, LLC
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(2008). Statistical Modeling by GIC. In: Information Criteria and Statistical Modeling. Springer Series in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-0-387-71887-3_6
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DOI: https://doi.org/10.1007/978-0-387-71887-3_6
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-71886-6
Online ISBN: 978-0-387-71887-3
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