Model comparison with missing data using MCMC and importance sampling
Model comparison with missing data using MCMC and importance sampling
复制标题
使用 MCMC 和重要性抽样与缺失数据进行模型比较
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
T. McKinley
中科院分区：
文献类型：
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作者：
Panayiota Touloupou;N. Alzahrani;P. Neal;S. Spencer;T. McKinley
在竞争统计模型之间进行选择是一个具有挑战性的问题，尤其是当竞争模型是非嵌套时。在本文中，我们通过设计一种结合 MCMC 和重要性采样的算法来提供一种简单的解决方案，以获得计算有效的边际似然估计，然后将其用于比较模型。该算法成功应用于纵向流行病和时间序列数据集，并显示出优于计算边际似然的现有方法。
Selecting between competing statistical models is a challenging problem especially when the competing models are non-nested. In this paper we offer a simple solution by devising an algorithm which combines MCMC and importance sampling to obtain computationally efficient estimates of the marginal likelihood which can then be used to compare the models. The algorithm is successfully applied to longitudinal epidemic and time series data sets and shown to outperform existing methods for computing the marginal likelihood.
影响因子：
2.1
作者：
Knock, Edward S.;O'Neill, Philip D.
通讯作者：
O'Neill, Philip D.