A note on conditional Akaike information for Poisson regression with random effects
A note on conditional Akaike information for Poisson regression with random effects
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DOI：
10.1214/12-ejs665
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发表时间：
2012-01-01
影响因子：
1.1
通讯作者：
Lian, Heng
中科院分区：
文献类型：
--
作者：
Lian, Heng
广义线性混合效应模型的一种流行的模型选择方法是Akaike信息准则，或AIC。其中，[7]指出了边际推理和条件推理之间的区别，这取决于研究的重点。对于后来由[5]推广的线性混合效应模型，导出了条件AIC。我们证明了类似的策略推广到具有随机效应的Poisson回归，其中条件AIC可以基于我们的观察得到。仿真研究验证了该判据的有效性。
A popular model selection approach for generalized linear mixed-effects models is the Akaike information criterion, or AIC. Among others, [7] pointed out the distinction between the marginal and conditional inference depending on the focus of research. The conditional AIC was derived for the linear mixed-effects model which was later generalized by [5]. We show that the similar strategy extends to Poisson regression with random effects, where conditional AIC can be obtained based on our observations. Simulation studies demonstrate the usage of the criterion.