In this article we investigate the performance of scan statistics based on moving medians, as test statistics for detecting a local change in population mean, for one and two dimensional normal data, in presence of outliers, when the population variance is unknown. For fixed window scan statistics, both the training sample and parametric bootstrap methods are employed for one and two dimensional normal data, in presence of one or two outliers. Multiple window scan statistics are implemented via the parametric bootstrap method for one and two dimensional normal data, in presence of one or two outliers. Numerical results are presented via simulation to evaluate the power of these scan statistics for detecting the local change in the population mean, for selected parameters of the models characterizing the local change in the population mean and models characterizing the occurrence of one or two outliers in the data. When the window size where the local change of the population mean has occurred is unknown, the multiple window scan statistics, implemented via the bootstrap method, performed quite well.

在本文中，我们调查基于移动中位数的扫描统计数据的性能，这是一种用于检测一维和二维正态数据（存在总体异常时，总体方差未知）的总体平均值局部变化的测试统计信息。对于固定的窗口扫描统计，在存在一两个异常值的情况下，针对一维和二维正常数据采用训练样本方法和参数自举方法。在存在一两个异常值的情况下，可以通过参数引导程序对一维和二维法线数据实现多个窗口扫描统计信息。通过仿真给出了数值结果，以评估这些扫描统计量用于检测总体均值的局部变化的能力，对于表征人口均值的局部变化的模型和表征数据中一个或两个异常值的模型的选定参数。当不知道发生总体均值局部变化的窗口大小时，通过自举方法实现的多窗口扫描统计数据表现良好。