论文标题
转换平均意见分数以避免误导基于排名的统计技术
Transformation of Mean Opinion Scores to Avoid Misleading of Ranked based Statistical Techniques
论文作者
论文摘要
当等级相关系数和基于排名的统计检验（作为非参数技术的子集）将其应用于主观收集的意见分数时可能会产生误导。这些技术假设数据至少在有序级别上测量，并定义一个分数序列，以代表绑定等级，而当它们具有相等的数字值时。 在本文中，我们表明，如上所述，绑定等级的定义不适合平均意见分数（MOS），可能是基于等级的统计技术的误导性结论。此外，我们介绍了一种通过考虑其95美元\％$置信区间的MOS值来克服此问题的方法。然后可以将秩相关系数和基于排名的统计检验安全地应用于转换值。我们还提供不同编程语言的开源软件包，以利用我们的转换方法在体验域中的应用。
The rank correlation coefficients and the ranked-based statistical tests (as a subset of non-parametric techniques) might be misleading when they are applied to subjectively collected opinion scores. Those techniques assume that the data is measured at least at an ordinal level and define a sequence of scores to represent a tied rank when they have precisely an equal numeric value. In this paper, we show that the definition of tied rank, as mentioned above, is not suitable for Mean Opinion Scores (MOS) and might be misleading conclusions of rank-based statistical techniques. Furthermore, we introduce a method to overcome this issue by transforming the MOS values considering their $95\%$ Confidence Intervals. The rank correlation coefficients and ranked-based statistical tests can then be safely applied to the transformed values. We also provide open-source software packages in different programming languages to utilize the application of our transformation method in the quality of experience domain.
