An adjusted boxplot for skewed distributions

By Mia Hubert, Ellen Vandervieren. Computational Statistics & Data Analysis, 2008

859 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, General Agricultural and Biological Sciences, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/44a385d6e6cf409db909eeb02565c41c9a0a80db

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

In General Agricultural and Biological Sciences

In Statistics and Probability

Citations in the corpus

Transcend: Detecting Concept Drift in Malware Classification Models (2017, methodology): “We then plot the boxplot [10], containing the p-values for each decision.”
Anomaly detection by robust statistics (2017, background): “) The value h in (10) plays the same role as in the MCD estimator.”
On an extreme value version of the Birnbaum-Saunders distribution (2012, background): “For more details about this adjusted boxplot for asymmetric data, see Hubert and Vandervieren (2008), an R package called robustbase and its function adjbox.”
Outlier detection for skewed data (2008, methodology): “Well-known high-breakdown and affine equivariant estimators of location and scatter are e.g. the MCD-estimator [2], the Stahel-Donoho estimator [3, 4], S-estimators [5, 6] and MM-estimators [7].”
The shifting boxplot. A boxplot based on essential summary statistics around the mean. (2010, background): “For example, Hubert and Vandervieren (2008) proposed an adjusted boxplot based on the medcouple measure of skewness to determine the length of the whiskers.”
Generalist Species Have a Central Role In a Highly Diverse Plant–Frugivore Network (2016, methodology): “We also computed an adjusted boxplot that better represents the skewed distribution of the species degrees (Hubert & Vandervieren 2008).”
Power Analysis and Effect Size in Mixed Effects Models: A Tutorial (2018, methodology): “As is general practice, RTs of error trials and outliers were excluded (outliers were detected using an adjusted boxplot for skewed distributions, Hubert & Vandervieren, 2008).”
Takeover Time in Highly Automated Vehicles: Noncritical Transitions to and From Manual Control (2017, methodology): “The box plots had their outlier thresholds adjusted to accommodate the lognormal distribution of the TOrt data by using the LIBRA library for MATLAB (Verboven & Hubert, 2005) and its method for robust boxplots for non-normally distributed data by Hubert and Vandervieren (2008).”
A Review and Comparison of Methods for Detecting Outliers in Univariate Data Sets (2006, methodology): “16 Vanderviere and Huber (2004) computed the average percentage of outliers beyond the lower and upper fence of two types of boxplots, the adjusted Boxplot and Tukey’s Boxplot, for several distributions and different sample sizes.”
40 years of boxplots (2010, background): “, 1999), explicit adjustments to the extremes to account for skewness (Hubert and Vandervieren, 2008), alternative definitions of fences (Dümbgen and Riedwyl, 2007) or alternative definitions of outliers (Carter et al.”
Using Conditional Association to Identify Locally Independent Item Sets (2016, background): “24 6 (Tukey, 1977; also Hubert & Vandervieren, 2008) and is used when distributions are skew.”