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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2451/14775
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| Title: | Semiparametric and Additive Model Selection Using an Improved Akaike
Information Criterion |
| Authors: | Simonoff, Jeffrey S.
Tsai, Chih-Ling |
| Keywords: | Goodness-of-fit
Kullback-Leibler discrepancy
Nonparametric regression
Smoothing spline regression estimator |
| Issue Date: | 1997 |
| Publisher: | Stern School of Business, New York University |
| Series/Report no.: | SOR-97-12 |
| Abstract: | An improved AIC-based criterion is derived for model selection in
general smoothing-based modeling, including semiparametric models and
additive models. Examples are provided of applications to
goodness-of-fit, smoothing parameter and variable selection in an
additive model and semiparametric models, and variable selection in a
model with a nonlinear function of linear terms. |
| URI: | http://hdl.handle.net/2451/14775 |
| Appears in Collections: | IOMS: Statistics Working Papers
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