基于改进Akaike信息准则的半参数和加性模型选择

NYU: IOMS: Statistics Working Papers (Topic) Pub Date : 1999-03-01 DOI:10.1080/10618600.1999.10474799

J. Simonoff, Chih-Ling Tsai

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引用次数: 49

摘要

摘要提出了一种改进的基于aic的模型选择准则，用于一般基于平滑的建模，包括半参数模型和加性模型。给出了在拟合优度、加性模型和半参数模型的平滑参数和变量选择、线性项非线性函数模型的变量选择等方面的应用实例。

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Semiparametric and Additive Model Selection Using an Improved Akaike Information Criterion

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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NYU: IOMS: Statistics Working Papers (Topic)

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