A0239
Title: When Tukey meets Chauvenet: A new boxplot criterion for outlier detection
Authors: Hongmei Lin - Shanghai University of International Business and Economics (China) [presenting]
Abstract: The box-and-whisker plot 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 deviation to perform the test, but it is often sensitive to the included outliers and hence is not robust. By combining Tukey's boxplot and Chauvenet's criterion, a new boxplot, namely the Chauvenet-type boxplot, is introduced with the fence coefficient determined by an exact control of the outside rate per observation. The 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.
