Akaike's information criterion in generalized estimating equations
Akaike's information criterion in generalized estimating equations
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DOI：
10.1111/j.0006-341x.2001.00120.x
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发表时间：
2001-03-01
期刊：
影响因子：
1.9
通讯作者：
Pan, W
中科院分区：
文献类型：
--
作者：
Pan, W
相关反应数据在生物医学研究中很常见。基于广义估计方程（GEE）的回归分析是处理此类数据的一种越来越重要的方法。然而，在GEE中似乎没有多少模式选择标准。众所周知的赤池信息准则（AIC）不能直接应用，因为AIC是基于最大似然估计，而GEE是基于非似然的。我们提出了一个修改AIC，其中的可能性被取代的准可能性和适当的调整是为惩罚条款。其性能进行了研究，通过模拟研究。为了说明，将该方法应用于真实的数据集。
Correlated response data are common in biomedical studies. Regression analysis based on the generalized estimating equations (GEE) is an increasingly important method for such data. However, there seem to be few model-selection criteria available in GEE. The well-known Akaike Information Criterion (AIC) cannot be directly applied since AIC is based on maximum likelihood estimation while GEE is nonlikelihood based. We propose a modification to AIC, where the likelihood is replaced by the quasi-likelihood and a proper adjustment is made for the penalty term. Its performance is investigated through simulation studies. For illustration, the method is applied to a real data set.