School of Mathematical Sciences Academic Lecture [2025] No. 080

(Seminar Series on High-Level University Development No. 1101)

Lecture Title: When Tukey meets Chauvenet: a new boxplot criterion for outlier detection

Lecturer: Associate Professor Hongmei Lin (Shanghai University of International Business and Economics)

Lecture Time: August 29, 2025, 10:00–11:00 AM

Venue: Room 514, Huixing Building

Abstract: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.

Lecturer’s Biography:Hongmei Lin is an Associate Professor and Ph.D. supervisor at the School of Statistics and Information, Shanghai University of International Business and Economics. She received her Ph.D. in Statistics from East China Normal University, and studied as a joint doctoral student at the University of California, Santa Barbara. She also visited the University of California, Riverside as a senior research scholar, and conducted multiple academic exchanges and visits at Hong Kong Baptist University and the Chinese University of Hong Kong. Her main research areas include nonparametric and semiparametric regression analysis, functional data analysis, and distributed statistical methods. She has published more than 20 papers in leading statistical journals such as Statistica Sinica and the Journal of Computational and Graphical Statistics. She has served as the principal investigator of one Youth Program and one General Program of the National Natural Science Foundation of China, one General Project of the Shanghai Natural Science Foundation, one Open Research Project of the Ministry of Education Key Laboratory, and one Key Course Project of Shanghai Municipality. In 2022, she was selected for the Shanghai “Morning Star Program” and awarded the title of “Morning Star Scholar.” In 2023, she was recognized as a young talent under the Shanghai “Oriental Talent Program.” She is currently a council member of the Chinese Society of Applied Statistics, and Vice President of its Education Statistics and Management Subcommittee.

All interested faculty and students are welcome to attend!

Inviter: Prof. Zhou Yan

School of Mathematical Sciences

August 26, 2025