Jelsema Casey M, Kwok Richard K, Peddada Shyamal D
Department of Biostatistics, School of Public Health, West Virginia University, Morgantown, West Virginia, 26505, USA.
Epidemiology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, 27709, USA.
J Stat Comput Simul. 2019;89(11):2121-2137. doi: 10.1080/00949655.2019.1610884. Epub 2019 Apr 30.
Large spatial datasets are typically modeled through a small set of knot locations; often these locations are specified by the investigator by arbitrary criteria. Existing methods of estimating the locations of knots assume their number is known , or are otherwise computationally intensive. We develop a computationally efficient method of estimating both the location and number of knots for spatial mixed effects models. Our proposed algorithm, Threshold Knot Selection (TKS), estimates knot locations by identifying clusters of large residuals and placing a knot in the centroid of those clusters. We conduct a simulation study showing TKS in relation to several comparable methods of estimating knot locations. Our case study utilizes data of particulate matter concentrations collected during the course of the response and clean-up effort from the 2010 oil spill in the Gulf of Mexico.
大型空间数据集通常通过一小组节点位置进行建模；这些位置通常由研究者根据任意标准指定。现有的估计节点位置的方法假定节点数量已知，否则计算量很大。我们开发了一种计算效率高的方法，用于估计空间混合效应模型中节点的位置和数量。我们提出的算法，阈值节点选择（TKS），通过识别大残差的聚类并在这些聚类的质心处放置一个节点来估计节点位置。我们进行了一项模拟研究，展示了TKS与几种估计节点位置的可比方法的关系。我们的案例研究利用了在2010年墨西哥湾漏油事件的应对和清理工作过程中收集的颗粒物浓度数据。
