Dimension reduction and alleviation of confounding for spatial generalized linear mixed models
Dimension reduction and alleviation of confounding for spatial generalized linear mixed models
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DOI：
10.1111/j.1467-9868.2012.01041.x
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发表时间：
2013-01-01
影响因子：
5.8
通讯作者：
Haran, Murali
中科院分区：
文献类型：
--
作者：
Hughes, John;Haran, Murali
非高斯空间数据在许多学科中非常常见。例如，计数数据在疾病制图中很常见，而二进制数据在生态学中很常见。在为这些数据拟合空间回归时，需要考虑相关性，以确保回归系数的可靠推断。空间广义线性混合模型提供了一种非常流行和灵活的方法来建模这样的数据，但这种模型有两个主要的缺点：由于空间混杂和高维空间随机效应，使完全贝叶斯推断这样的模型计算上具有挑战性的方差膨胀。我们提出了一个新的参数化的空间广义线性混合模型，简化了空间混杂和速度的计算，大大减少了空间随机效应的维数。我们说明了我们的方法模拟二进制，计数和高斯空间数据集，并到一个大的婴儿死亡率数据集的应用。
Non-Gaussian spatial data are very common in many disciplines. For instance, count data are common in disease mapping, and binary data are common in ecology. When fitting spatial regressions for such data, one needs to account for dependence to ensure reliable inference for the regression coefficients. The spatial generalized linear mixed model offers a very popular and flexible approach to modelling such data, but this model suffers from two major shortcomings: variance inflation due to spatial confounding and high dimensional spatial random effects that make fully Bayesian inference for such models computationally challenging. We propose a new parameterization of the spatial generalized linear mixed model that alleviates spatial confounding and speeds computation by greatly reducing the dimension of the spatial random effects. We illustrate the application of our approach to simulated binary, count and Gaussian spatial data sets, and to a large infant mortality data set.