Model choice in generalised linear models: A Bayesian approach via Kullback-Leibler projections
Model choice in generalised linear models: A Bayesian approach via Kullback-Leibler projections
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DOI：
10.1093/biomet/85.1.29
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发表时间：
1998-03-01
期刊：
影响因子：
2.7
通讯作者：
Robert, CP
中科院分区：
文献类型：
--
作者：
Goutis, C;Robert, CP
我们提出了一种比较模型的通用贝叶斯方法。该方法基于两个模型系列之间的 Kullback-Leibler 距离，一个模型嵌套在另一个模型中。对于完整模型的每个参数值，我们计算模型到受限参数空间的投影以及相应的最小距离。从最小距离的后验分布，我们可以判断更简约的模型是否合适。我们展示了如何实现投影方法来进行广义线性模型选择，并提出了一些马尔可夫链蒙特卡罗算法，用于在不太容易处理的情况下的实际实现。我们用例子来说明该方法。
We propose a general Bayesian method of comparing models. The approach is based on the Kullback-Leibler distance between two families of models, one nested within the other. For each parameter value of a full model, we compute the projection of the model to the restricted parameter space and the corresponding minimum distance. From the posterior distribution of the minimum distance, we can judge whether or not a more parsimonious model is appropriate. We show how the projection method can be implemented for generalised linear model selection and we propose some Markov chain Monte Carlo algorithms for its practical implementation in less tractable cases. We illustrate the method with examples.