SPATIAL SCAN STATISTICS FOR MODELS WITH OVERDISPERSION AND INFLATED ZEROS
SPATIAL SCAN STATISTICS FOR MODELS WITH OVERDISPERSION AND INFLATED ZEROS
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DOI：
10.5705/ss.2013.220w
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发表时间：
2015-01-01
影响因子：
1.4
通讯作者：
Pinto, Leticia P.
中科院分区：
文献类型：
--
作者：
de Lima, Max S.;Duczmal, Luiz H.;Pinto, Leticia P.
空间扫描统计量是检测和监测空间疾病聚集的重要方法之一。通常假定疾病病例遵循泊松分布或二项分布。然而，在实践中，病例计数数据集经常出现过多的零和/或过度分散，导致违反那些常用的模型，增加了I型错误的发生。本文描述了用零膨胀双泊松（ZIDP）模型对空间扫描统计量的修正，以减少I型误差，同时适应零的过量和过色散。通过期望最大化算法估计模型的空参数和备选参数，并通过快速双Bootstrap检验获得p值。一个应用程序提出了汉森病的数据在巴西亚马逊。
The Spatial Scan Statistic is one of the most important methods for detecting and monitoring spatial disease clusters. Usually it is assumed that disease cases follow a Poisson or Binomial distribution. In practice, however, case count datasets frequently present an excess of zeroes and/or overdispersion, resulting in the violation of those commonly used models, increasing type I error occurrence. This paper describes a modification of the Spatial Scan Statistic with the Zero Inflated Double Poisson (ZIDP) model to reduce type I error, accommodating simultaneously an excess of zeroes and overdispersion. The null and alternative model parameters are estimated by the Expectation-Maximization algorithm and the p-value is obtained through the Fast Double Bootstrap Test. An application is presented for Hanseniasis data in the Brazilian Amazon.