论文标题
在功能数据中检测空间簇：新的扫描统计方法
Detecting spatial clusters in functional data: new scan statistic approaches
论文作者
论文摘要
我们已经开发了两个扫描统计数据，用于检测空间中索引的功能数据簇。第一种方法是基于对方差函数分析的适应，第二种方法基于单变量数据的无分布空间扫描统计量。在一项仿真研究中，无分布方法的执行始终比非参数功能扫描统计量更好，而ANOVA的适应性对于具有正常或准正常分布的数据也更好。与非参数方法相比，我们的方法可以检测到较小的空间簇。最后，我们将扫描统计数据用于功能数据，以在1998 - 2013年期间（分为季度）搜索法国异常失业率的空间集群。
We have developed two scan statistics for detecting clusters of functional data indexed in space. The first method is based on an adaptation of a functional analysis of variance and the second one is based on a distribution-free spatial scan statistic for univariate data. In a simulation study, the distribution-free method always performed better than a nonparametric functional scan statistic, and the adaptation of the anova also performed better for data with a normal or a quasi-normal distribution. Our methods can detect smaller spatial clusters than the nonparametric method. Lastly, we used our scan statistics for functional data to search for spatial clusters of abnormal unemployment rates in France over the period 1998-2013 (divided into quarters).
