Model choice: A minimum posterior predictive loss approach
Model choice: A minimum posterior predictive loss approach
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DOI：
10.1093/biomet/85.1.1
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发表时间：
1998-03-01
期刊：
影响因子：
2.7
通讯作者：
Ghosh, SK
中科院分区：
文献类型：
--
作者：
Gelfand, AE;Ghosh, SK
模型选择是数据集分析中的一项基本活动，也是一项备受讨论的活动。引入随机效应的非嵌套分层模型可能无法用经典方法处理。使用预测分布的贝叶斯方法可以使用，但正式的解决方案，其中包括贝叶斯因素作为一个特殊的情况下，可以批评。我们提出了一个预测标准，其目标是很好地预测所观察到的数据的复制，但通过对观察值的保真度来调节。我们得到这个标准，最小化后验损失为一个给定的模型，然后，考虑模型，选择一个最小化这个标准。对于一个广泛的损失，标准出现的形式分为一个拟合优度项和惩罚项。我们说明了它的性能与应用程序涉及住宅物业交易的大型数据集。
Model choice is a fundamental and much discussed activity in the analysis of datasets. Nonnested hierarchical models introducing random effects may not be handled by classical methods. Bayesian approaches using predictive distributions can be used though the formal solution, which includes Bayes factors as a special case, can be criticised. We propose a predictive criterion where the goal is good prediction of a replicate of the observed data but tempered by fidelity to the observed values. We obtain this criterion by minimising posterior loss for a given model and then, for-models under consideration, selecting the one which minimises this criterion. For a broad range of losses, the criterion emerges as a form partitioned into a goodness-of-fit term and a penalty term. We illustrate its performance with an application to a large dataset involving residential property transactions.