A note on composite likelihood inference and model selection
A note on composite likelihood inference and model selection
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DOI：
10.1093/biomet/92.3.519
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发表时间：
2005-09-01
期刊：
影响因子：
2.7
通讯作者：
Vidoni, P
中科院分区：
文献类型：
--
作者：
Varin, C;Vidoni, P
复合似然由有效似然对象的组合组成，通常与较小的数据子集相关。复合似然的优点是降低了计算复杂性，因此即使在使用标准似然或贝叶斯方法不可行的情况下，也可以处理大数据集和非常复杂的模型。在本文中，我们的目标是提出一种综合的、通用的方法来进行推理和模型选择，使用复合似然方法。特别是，我们引入了一种基于复合似然的模型选择的信息准则。我们还描述了通过动态广义线性模型对计数的时间序列建模的应用，以及对著名的Old Faithful间歇泉数据集的分析。
A composite likelihood consists of a combination of valid likelihood objects, usually related to small subsets of data. The merit of composite likelihood is to reduce the computational complexity so that it is possible to deal with large datasets and very complex models, even when the use of standard likelihood or Bayesian methods is not feasible. In this paper, we aim to suggest an integrated, general approach to inference and model selection using composite likelihood methods. In particular, we introduce an information criterion for model selection based on composite likelihood. We also describe applications to the modelling of time series of counts through dynamic generalised linear models and to the analysis of the well-known Old Faithful geyser dataset.