Suppr超能文献

我们的数据是对称的吗?

Are our data symmetric?

作者信息

Mandrekar Sumithra J, Mandrekar Jayawant N

机构信息

Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA.

出版信息

Stat Methods Med Res. 2003 Dec;12(6):505-13. doi: 10.1191/0962280203sm346oa.

Abstract

Skewness indicates a lack of symmetry in a distribution. Knowing the symmetry of the underlying data is essential for parametric analysis, fitting distributions or doing transformations to the data. The coefficient of skewness is the commonly used measure to identify a lack of symmetry in the underlying data, although graphical procedures can also be effective. We discuss three different methods to assess skewness: traditional coefficient of skewness index, skewness index based on the L-moments discussed by Hosking and the asymptotic test of symmetry developed by Randles et al. With this work, we provide easy-to-implement S-PLUS functions as well as discuss the advantages and shortcomings of each technique.

摘要

偏度表明分布缺乏对称性。了解基础数据的对称性对于参数分析、拟合分布或对数据进行变换至关重要。偏度系数是用于识别基础数据缺乏对称性的常用度量,尽管图形方法也可能有效。我们讨论三种评估偏度的不同方法:传统的偏度系数指数、基于霍斯金讨论的L矩的偏度指数以及兰德斯等人开发的对称性渐近检验。通过这项工作,我们提供了易于实现的S-PLUS函数,并讨论了每种技术的优缺点。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验