Information criteria for discriminating among alternative regression models / BEBR No. 455
Information criteria for discriminating among alternative regression models / BEBR No. 455
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区分替代回归模型的信息标准 / BEBR No. 455
DOI：
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发表时间：
1978
期刊：
影响因子：
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通讯作者：
T. Sawa
中科院分区：
文献类型：
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作者：
T. Sawa
提出了一些用于区分替代回归模型的决策规则并相互比较。它们本质上基于 Akaike 信息准则以及 Kullback-Leibler 信息准则 (KLIC)：即，假设模型与真实未知结构之间的距离由 KLIC 测量。所提出的标准将参数的简约性与拟合优度结合起来。它们与传统标准的关系是根据新的公正性概念进行讨论的。
Some decision rules for discriminating among alternative regression models are proposed and mutually compared. They are essentially based on the Akaike Information Criterion as well as the Kullback-Leibler Information Criterion (KLIC) : namely, the distance between a postulated model and the true unknown structure is measured by the KLIC. The proposed criteria combine the parsimony of parameters with the goodness of fit. Their relationships with conventional criteria are discussed in terms of a new concept of unbiasedness .