Hossain Md Monir, Lawson Andrew B
Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, 800 Sumter Street, Columbia, SC 29208, USA.
Environ Ecol Stat. 2010 Mar 1;17(1):73-95. doi: 10.1007/s10651-008-0102-z.
This paper extends the spatial local-likelihood model and the spatial mixture model to the space-time (ST) domain. For comparison, a standard random effect space-time (SREST) model is examined to allow evaluation of each model's ability in relation to cluster detection. To pursue this evaluation, we use the ST counterparts of spatial cluster detection diagnostics. The proposed criteria are based on posterior estimates (e.g., misclassification rate) and some are based on post-hoc analysis of posterior samples (e.g., exceedance probability). In addition, we examine more conventional model fit criteria including mean square error (MSE). We illustrate the methodology with a real ST dataset, Georgia throat cancer mortality data for the years 1994-2005, and a simulated dataset where different levels and shapes of clusters are embedded. Overall, it is found that conventional SREST models fair well in ST cluster detection and in goodness-of-fit, while for extreme risk detection the local likelihood ST model does best.
本文将空间局部似然模型和空间混合模型扩展到时空（ST）领域。为作比较，研究了标准随机效应时空（SREST）模型，以评估每个模型在聚类检测方面的能力。为进行此评估，我们使用空间聚类检测诊断方法的时空对应方法。所提出的标准基于后验估计（例如误分类率），有些基于后验样本的事后分析（例如超越概率）。此外，我们研究了更传统的模型拟合标准，包括均方误差（MSE）。我们用一个真实的时空数据集（1994 - 2005年佐治亚州喉癌死亡率数据）和一个嵌入了不同级别和形状聚类的模拟数据集来说明该方法。总体而言，发现传统的SREST模型在时空聚类检测和拟合优度方面表现良好，而对于极端风险检测，局部似然时空模型表现最佳。
