Clustering Random Curves Under Spatial Interdependence With Application to Service Accessibility
Clustering Random Curves Under Spatial Interdependence With Application to Service Accessibility
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DOI：
10.1080/00401706.2012.657106
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发表时间：
2012-05-01
期刊：
影响因子：
2.5
通讯作者：
Serban, Nicoleta
中科院分区：
文献类型：
--
作者：
Jiang, Huijing;Serban, Nicoleta
服务可达性是指在由多个地理分布的服务站点组成的服务网络中，社区对邻近站点位置的访问。利用新的统计方法，本文估计和分类服务的可访问性模式不同，在一个大的地理区域（格鲁吉亚），并在一段时间内的16年。这项研究的重点是金融服务，但它通常适用于任何其他服务业务。为此，我们引入了一个基于模型的方法聚类随机时变函数，空间上相互依赖。底层的聚类模型是非参数的，具有空间相关的误差。我们还假设，聚类成员是一个实现从马尔可夫随机场。在这些模型的假设下，我们借用信息对应于附近的空间位置的功能，从而提高估计精度的集群效应和集群成员的模拟研究中所示。补充材料，包括估计算法，数据的附加地图，以及用于分析我们案例研究中数据的C++计算机程序，都可以在线获得。
Service accessibility is defined as the access of a community to the nearby site locations in a service network consisting of multiple geographically distributed service sites. Leveraging new statistical methods, this article estimates and classifies service accessibility patterns varying over a large geographic area (Georgia) and over a period of 16 years. The focus of this study is on financial services but it generally applies to any other service operation. To this end, we introduce a model-based method for clustering random time-varying functions that are spatially interdependent. The underlying clustering model is nonparametric with spatially correlated errors. We also assume that the clustering membership is a realization from a Markov random field. Under these model assumptions, we borrow information across functions corresponding to nearby spatial locations resulting in enhanced estimation accuracy of the cluster effects and of the cluster membership as shown in a simulation study. Supplementary materials including the estimation algorithm, additional maps of the data, and the C++ computer programs for analyzing the data in our case study are available online.