Hossain Monir Md, Lawson Andrew B
Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, USA.
Stat Med. 2006 Mar 15;25(5):771-86. doi: 10.1002/sim.2401.
The focus of this paper is the development of a range of cluster detection diagnostics that can be used to assess the degree to which a clustering method recovers the true clustering behaviour of small area data. The diagnostics proposed range from individual region specific diagnostics to neighbourhood diagnostics, and assume either individual region risk as focus, or concern areas of maps defined to be clustered and the recovery ability of methods. A simulation-based comparison is made between a small set of count data models: local likelihood, BYM and Lawson and Clark. It is found that local likelihood has good performance across a range of criteria when a CAR prior is assumed for the lasso parameter.
本文的重点是开发一系列聚类检测诊断方法，这些方法可用于评估聚类方法在何种程度上恢复小区域数据的真实聚类行为。所提出的诊断方法范围从单个区域特定诊断到邻域诊断，并假设以单个区域风险为重点，或者关注定义为聚类的地图区域以及方法的恢复能力。对一小部分计数数据模型进行了基于模拟的比较：局部似然法、BYM法以及劳森和克拉克法。研究发现，当对套索参数假设一个条件自回归（CAR）先验时，局部似然法在一系列标准下都具有良好的性能。
