Bias-corrected AIC for selecting variables in multinomial logistic regression models☆
Bias-corrected AIC for selecting variables in multinomial logistic regression models☆
复制标题
用于在多项逻辑回归模型中选择变量的偏差校正 AIC☆
DOI：
10.1016/j.laa.2012.01.018
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发表时间：
2012
影响因子：
1.1
通讯作者：
K. Satoh
中科院分区：
文献类型：
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作者：
H. Yanagihara;K. Kamo;S. Imori;K. Satoh
本文研究了多项Logistic回归模型中变量选择的Akaike信息准则（AIC）的偏差校正问题。为了简化偏差校正的AIC公式，我们通过负对数似然函数的偏导数的期望来计算AIC对风险函数的偏差。其结果是，我们可以表示偏差校正AIC的偏差校正项，只有三个矩阵组成的负对数似然函数的二阶，三阶和四阶导数。通过进行数值研究，我们验证了所提出的偏差校正AIC比原始AIC的性能更好。
In this paper, we consider the bias correction of Akaike’s information criterion (AIC) for selecting variables in multinomial logistic regression models. For simplifying a formula of the bias-corrected AIC, we calculate the bias of the AIC to a risk function through the expectations of partial derivatives of the negative log-likelihood function. As a result, we can express the bias correction term of the bias-corrected AIC with only three matrices consisting of the second, third, and fourth derivatives of the negative log-likelihood function. By conducting numerical studies, we verify that the proposed bias-corrected AIC performs better than the crude AIC.
逻辑回归模型中 AIC 的偏差校正。
DOI：
--
发表时间：
2003
期刊：
Journal of Statistical Planning and Inference 115(2)
影响因子：
--
作者：
Yanagihara;H
通讯作者：
H