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Please use this identifier to cite or link to this item: http://hdl.handle.net/2451/14775

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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