Conditional Akaike information under generalized linear and proportional hazards mixed models
Conditional Akaike information under generalized linear and proportional hazards mixed models
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DOI：
10.1093/biomet/asr023
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发表时间：
2011-09-01
期刊：
影响因子：
2.7
通讯作者：
Vaida, F.
中科院分区：
文献类型：
--
作者：
Donohue, M. C.;Overholser, R.;Vaida, F.
我们研究模型选择聚类数据，当重点是集群特定的推理。这些数据通常使用随机效应建模，Vaida & Blanchard（2005）提出了条件赤池信息，并用于推导线性混合模型下的信息标准。在这里，我们扩展的方法，广义线性和比例风险混合模型。在正常的线性混合模型之外，精确的计算是不可用的，我们采用渐近近似。在干扰参数存在的情况下，提出了一种轮廓条件赤池信息。Bootstrap方法被认为是在有限样本中的潜在优势。仿真结果表明，性能的引导和分析标准是可比的，与引导展示了一些优势，较大的集群大小。所提出的标准被应用到两个癌症数据集选择模型时，集群特定的推理是感兴趣的。
We study model selection for clustered data, when the focus is on cluster specific inference. Such data are often modelled using random effects, and conditional Akaike information was proposed in Vaida & Blanchard (2005) and used to derive an information criterion under linear mixed models. Here we extend the approach to generalized linear and proportional hazards mixed models. Outside the normal linear mixed models, exact calculations are not available and we resort to asymptotic approximations. In the presence of nuisance parameters, a profile conditional Akaike information is proposed. Bootstrap methods are considered for their potential advantage in finite samples. Simulations show that the performance of the bootstrap and the analytic criteria are comparable, with bootstrap demonstrating some advantages for larger cluster sizes. The proposed criteria are applied to two cancer datasets to select models when the cluster-specific inference is of interest.