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Semiparametric and Additive Model Selection Using an Improved Akaike Information Criterion

Jeffrey S. Simonoff and Chih-Ling Tsai
Journal of Computational and Graphical Statistics
Vol. 8, No. 1 (Mar., 1999), pp. 22-40
DOI: 10.2307/1390918
Stable URL: http://www.jstor.org/stable/1390918
Page Count: 19
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Semiparametric and Additive Model Selection Using an Improved Akaike Information Criterion
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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.

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