摘要翻译：
空间扫描统计作为一种识别疾病热点的工具，在流行病学和医学研究中得到了广泛的应用。经典的空间扫描统计假设不同地点的病例数具有独立的泊松分布，而在实际应用中，数据可能表现出过度分散和空间相关性。在本工作中，我们研究了空间扫描统计量在存在过色散和空间相关时的行为，并提出了一种修正的空间扫描统计量来解释这一点。理论结果表明，忽略过频散和空间相关性会增加误报率，并通过仿真研究验证了这一点。仿真研究还表明，改进后的算法可以大大降低误报率。两个涉及新墨西哥州脑癌病例和法国水痘发病率数据的数据例子被用来说明改进程序的实际相关性。
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英文标题：
《Accounting for spatial correlation in the scan statistic》
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作者：
Ji Meng Loh, Zhengyuan Zhu
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最新提交年份：
2007
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分类信息：

一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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英文摘要：
  The spatial scan statistic is widely used in epidemiology and medical studies as a tool to identify hotspots of diseases. The classical spatial scan statistic assumes the number of disease cases in different locations have independent Poisson distributions, while in practice the data may exhibit overdispersion and spatial correlation. In this work, we examine the behavior of the spatial scan statistic when overdispersion and spatial correlation are present, and propose a modified spatial scan statistic to account for that. Some theoretical results are provided to demonstrate that ignoring the overdispersion and spatial correlation leads to an increased rate of false positives, which is verified through a simulation study. Simulation studies also show that our modified procedure can substantially reduce the rate of false alarms. Two data examples involving brain cancer cases in New Mexico and chickenpox incidence data in France are used to illustrate the practical relevance of the modified procedure.
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PDF链接：
https://arxiv.org/pdf/712.1458