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A scan statistic for continuous data based on the normal probability model

机译:基于正态概率模型的连续数据扫描统计

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摘要

Temporal, spatial and space-time scan statistics are commonly used to detect and evaluate the statistical significance of temporal and/or geographical disease clusters, without any prior assumptions on the location, time period or size of those clusters. Scan statistics are mostly used for count data, such as disease incidence or mortality. Sometimes there is an interest in looking for clusters with respect to a continuous variable, such as lead levels in children or low birth weight. For such continuous data, we present a scan statistic where the likelihood is calculated using the the normal probability model. It may also be used for other distributions, while still maintaining the correct alpha level. In an application of the new method, we look for geographical clusters of low birth weight in New York City.
机译:时间,空间和时空扫描统计数据通常用于检测和评估时间和/或地理疾病群集的统计显着性,而无需事先对这些群集的位置,时间段或大小进行任何假设。扫描统计数据主要用于计数数据,例如疾病发生率或死亡率。有时有兴趣寻找与连续变量有关的聚类,例如儿童的铅水平或低出生体重。对于此类连续数据,我们提供了一种扫描统计信息,其中使用法线概率模型计算了可能性。它也可以用于其他发行版,同时仍保持正确的Alpha级别。在新方法的应用中,我们寻找纽约市低出生体重的地理集群。

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