报告题目：When Tukey meets Chauvenet: A new boxplot criterion for outlier detection
报告人：张日权 教授 上海对外经贸大学统计与数据科学学院
报告时间：2025年10月20日 10:00-11:00
报告地点：腾讯会议：480-512-543
校内联系人：杜明月 mingydu@jlu.edu.cn
报告摘要：The box-and-whisker plot, introduced by Tukey (1977), is one of the most popular graphical methods in descriptive statistics. On the other hand, however, Tukey's boxplot is free of sample size, yielding the so-called “one-size-fits-all”fences for outlier detection. Although improvements on the sample size adjusted boxplots do exist in the literature, most of them are either not easy to implement or lack justification. As another common rule for outlier detection, Chauvenet's criterion uses the sample mean and standard derivation to perform the test, but it is often sensitive to the included outliers and hence is not robust. In this paper, by combining Tukey's boxplot and Chauvenet's criterion, we introduce a new boxplot, namely the Chauvenet-type boxplot, with the fence coefficient determined by an exact control of the outside rate per observation. Our new outlier criterion not only maintains the simplicity of the boxplot from a practical perspective, but also serves as a robust Chauvenet's criterion. Simulation study and a real data analysis on the civil service pay adjustment in Hong Kong demonstrate that the Chauvenet-type boxplot performs extremely well regardless of the sample size, and can therefore be highly recommended for practical use to replace both Tukey's boxplot and Chauvenet's criterion. Lastly, we also develop a new R package ‘C.boxplot’ that can be implemented very user-friendly, and moreover make the source files and example codes freely available.
报告人简介：上海对外经贸大学统计与数据科学学院院长，教授、博士生导师；中国现场统计研究会大数据统计分会理事长。《统计研究》《数理统计与管理》等期刊编委；《应用概率统计》期刊第八届、第九届副主编。曾任华东师范大学统计学院院长，教育部统计与数据科学前沿理论及应用重点实验室主任；主要研究方向大数据统计、高维数据分析、函数型数据分析、统计机器学习等，发表论文230余篇，主持国家自科重点项目、上海市自科重点项目等各类项目20余项。
