Spatial disease mapping using directed acyclic graph auto-regressive (DAGAR) models.
Spatial disease mapping using directed acyclic graph auto-regressive (DAGAR) models.
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DOI：
10.1214/19-ba1177
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发表时间：
2019-12
影响因子：
4.4
通讯作者：
Gao L
中科院分区：
文献类型：
--
作者：
Datta A;Banerjee S;Hodges JS;Gao L
区域聚集疾病发病率数据的分层模型通常涉及区域特异性潜在随机效应，其被联合建模为具有多变量高斯分布。协方差或精度矩阵包含区域之间的空间依赖性。精度矩阵的常见选择包括广泛使用的ICAR模型，它是奇异的，它的非奇异扩展缺乏可解释性。我们提出了一个新的参数模型的精度矩阵的基础上有向无环图（DAG）表示的空间依赖。我们的模型保证了正定性，因此，除了作为区域空间相关随机效应的有效先验外，还可以直接对图像和网络等相关数据的结果进行建模。理论结果建立了我们模型中的参数与随机效应的方差和协方差之间的联系。大量的模拟研究表明，我们的模型的改进的可解释性收获的好处，在准确地恢复潜在的空间随机效应，以及空间协方差参数的推断。在适度的空间相关性下，我们的模型远远优于CAR模型，而当空间相关性较强时，性能相似。我们还评估了选择的顺序在DAG建设的理论和实证结果证明了我们的模型的鲁棒性的敏感性。我们还提出了一个大规模的公共卫生应用程序，展示了该模型的竞争力。
Hierarchical models for regionally aggregated disease incidence data commonly involve region specific latent random effects that are modeled jointly as having a multivariate Gaussian distribution. The covariance or precision matrix incorporates the spatial dependence between the regions. Common choices for the precision matrix include the widely used ICAR model, which is singular, and its nonsingular extension which lacks interpretability. We propose a new parametric model for the precision matrix based on a directed acyclic graph (DAG) representation of the spatial dependence. Our model guarantees positive definiteness and, hence, in addition to being a valid prior for regional spatially correlated random effects, can also directly model the outcome from dependent data like images and networks. Theoretical results establish a link between the parameters in our model and the variance and covariances of the random effects. Substantive simulation studies demonstrate that the improved interpretability of our model reaps benefits in terms of accurately recovering the latent spatial random effects as well as for inference on the spatial covariance parameters. Under modest spatial correlation, our model far outperforms the CAR models, while the performances are similar when the spatial correlation is strong. We also assess sensitivity to the choice of the ordering in the DAG construction using theoretical and empirical results which testify to the robustness of our model. We also present a large-scale public health application demonstrating the competitive performance of the model.
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DOI：
10.1111/j.1467-9868.2012.01041.x
发表时间：
2013-01-01
影响因子：
5.8
作者：
Hughes, John;Haran, Murali
通讯作者：
Haran, Murali
影响因子：
2.7
作者：
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通讯作者：
Kooperberg, C
DOI：
10.1111/1467-9868.00201
发表时间：
1999-01-01
影响因子：
5.8
作者：
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通讯作者：
Higdon, D
DOI：
10.1214/16-aoas931
发表时间：
2016-09
期刊：
The annals of applied statistics
影响因子：
--
作者：
Datta A;Banerjee S;Finley AO;Hamm NAS;Schaap M
通讯作者：
Schaap M
影响因子：
3.7
作者：
Datta A;Banerjee S;Finley AO;Gelfand AE
通讯作者：
Gelfand AE