When studying the properties of various physical objects, experimental data often contain gross errors (outliers) which can lead to significant distortions of the results of statistical processing of such data. For this reason, statistical procedures are being developed that are protected from the presence of outliers in observations. In this paper, two types of robust estimates of the scale parameter, which characterizes the spread (variability) of the random variable under study, are considered. The proposed estimates are asymptotically normally distributed, have bounded influence functions, and therefore, unlike the standard deviation estimates, are protected from the presence of outliers in the sample. The results of comparing the estimates of the scale parameter by the efficiency for different observation models, in particular, for the Gauss model with large-scale contamination, are presented.

在研究各种物理对象的属性时，实验数据通常包含严重错误（异常值），这可能导致此类数据的统计处理结果出现重大失真。因此，正在开发统计程序，以防止观测值中存在异常值。在本文中，考虑了两种类型的尺度参数的鲁棒估计，这些鲁棒性估计表征了所研究的随机变量的扩展（可变性）。提议的估计是渐近正态分布的，具有有限的影响函数，因此，与标准偏差估计不同，它可以防止样本中存在异常值。比较不同观测模型的效率对比例参数的估计值的结果，尤其是