Hierarchical Bayesian modeling of spatio-temporal area-interaction processes
Hierarchical Bayesian modeling of spatio-temporal area-interaction processes
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DOI：
10.1016/j.csda.2021.107349
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发表时间：
2022-03
期刊：
影响因子：
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通讯作者：
Jiaxun Chen;A. Micheas;S. Holan
中科院分区：
文献类型：
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作者：
Jiaxun Chen;A. Micheas;S. Holan
为了对具有离散时间戳的空间点模式进行建模，提出了一种灵活的时空区域交互点过程。特别是，当新的点模式从前一个点模式产生时，该模型适合描述点模式之间随时间的依赖性。还实现了分层模型，以便合并模型参数的底层演化过程。对于参数估计，使用吉布斯采样器内的双 Metropolis-Hastings。通过模拟研究评估估计算法的性能。最后，通过模拟研究和对 2002 年至 2019 年美国自然引起的野火数据的应用，演示了点模式预测程序。
To model spatial point patterns with discrete time stamps a flexible spatio-temporal area-interaction point process is proposed. In particular, this model is suitable for describing the dependency between point patterns over time, when the new point pattern arises from the previous point pattern. A hierarchical model is also implemented in order to incorporate the underlying evolution process of the model parameters. For parameter estimation, a double Metropolis-Hastings within Gibbs sampler is used. The performance of the estimation algorithm is evaluated through a simulation study. Finally, the point pattern forecasting procedure is demonstrated through a simulation study and an application to United States natural caused wildfire data from 2002 to 2019.