A spatial scan statistic for compound Poisson data
A spatial scan statistic for compound Poisson data
复制标题
复合泊松数据的空间扫描统计
作者：
R. Rosychuk;Hsing
空间聚类检测的主题在20世纪80年代末和90年代初受到了统计学的关注。一直致力于开发用于检测生物科学、天文学和流行病学中病例和事件的空间聚集的方法。最近，研究已经检查了检测与个体的健康状况相关联的相关计数数据的集群。这种方法允许研究人员检查疾病相关事件的空间关系，而不仅仅是事件或流行病例。我们介绍了一个空间扫描测试，确定集群的事件在研究区域。由于单个病例可能有多个（重复）事件，因此我们将测试基于复合泊松模型。我们说明了我们的方法，用于急诊科就诊的聚类检测，其中个人可能会进行多个疾病相关的就诊。版权所有© 2013约翰威利父子有限公司.
The topic of spatial cluster detection gained attention in statistics during the late 1980s and early 1990s. Effort has been devoted to the development of methods for detecting spatial clustering of cases and events in the biological sciences, astronomy and epidemiology. More recently, research has examined detecting clusters of correlated count data associated with health conditions of individuals. Such a method allows researchers to examine spatial relationships of disease‐related events rather than just incident or prevalent cases. We introduce a spatial scan test that identifies clusters of events in a study region. Because an individual case may have multiple (repeated) events, we base the test on a compound Poisson model. We illustrate our method for cluster detection on emergency department visits, where individuals may make multiple disease‐related visits. Copyright © 2013 John Wiley & Sons, Ltd.
影响因子：
5
作者：
Bruce W. Turnbull;Eric J. Iwano;William S. Burnett;Holly L. Howe;Larry C. Clark
通讯作者：
Bruce W. Turnbull;Eric J. Iwano;William S. Burnett;Holly L. Howe;Larry C. Clark