A Comparison of Some New Measures of Skewness

By Guy Brys, Mia Hubert, Anja Struyf. 2003

98 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/84bb0997fb45ab789d9c6fad2c4773c5ad16632e

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In Statistics and Probability

Citations in the corpus

Nonparametric Multivariate Descriptive Measures Based on Spatial Quantiles (2004, background): “tF (r, s) = vF (r) vF (s) , 0 < r < s < 1, (15)”
Cokriging particle size fractions of the soil (2007, methodology): “We therefore computed the octile skew of the data (Brys et al., 2003).”
Statistics of vertical vorticity, divergence, and strain in a developed submesoscale turbulence field (2013, methodology): “This illustrates the fact that Pearson’s skewness used by R01 and in this study may not be the most robust measure of distribution asymmetry, as it may be excessively affected by the outliers [Brys et al., 2003].”
A Robust Measure of Skewness (2004, background): “As F and G have interval support, they have a strictly monotone quantile function, hence we can find for any couple (x1, x2) with x1 ≤ 0 ≤ x2 a unique couple (y1, y2) with y1 ≤ 0 ≤ y2 such that x1 = F −1(p) x2 = F−1(q) y1 = G−1(p) y2 = G−1(q) with p ∈ [0, 1 2 ] and q ∈ [ 2 , 1].”
Outlier detection for skewed data (2008, background): “In [26], it is shown that the MC is not too conservative (such that asymmetry of the main group can be found) but robust enough (asymmetry due to outliers is detected when the outliers are far enough in the tails of the distribution).”
An empirical power comparison of univariate goodness-of-fit tests for normality (2010, background): “This set of distributions was specifically considered in order to identify which normality tests are less sensitive to extreme observations that may be present in an underlying normal data sample.”