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International Journal of Data Science and Analytics
Published in: International Journal of Data Science and Analytics 1/2019

06-03-2018 | Regular Paper

An alternative data analytic approach to measure the univariate and multivariate skewness

Authors: Ravindra Khattree, Manoj Bahuguna

Published in: International Journal of Data Science and Analytics | Issue 1/2019

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Abstract

We introduce a new measure of univariate skewness of a distribution or data based on quantiles and by using the concepts of even and odd functions. Based on this new measure, we then suggest an approach to define the multivariate skewness for the multivariate distributions and multidimensional data and accordingly suggest a measure for it. Using numerous data sets, we illustrate that Mardia’s measure of multivariate skewness appears to be ambiguous in what it actually measures and show that our measure not only has an intuitive appeal, it also unambiguously quantifies what one would view as the multivariate skewness. Approach presented here is data analytic and can be implemented on a computer. Based on the idea of orthogonal transformation of the data, we also suggest another multivariate measure of skewness which may be simpler to compute.
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Appendix
Available only for authorised users
Footnotes
1
Another way to look at this is to realize that, with obvious notations, for a vector \(\begin{bmatrix} \pmb {Y} \\ \pmb {X} \end{bmatrix}\), \(\pmb {X}\) and \(\pmb {Y}-\pmb {\Sigma _{yx}\pmb {\Sigma }_{xx}^{-1} \pmb {X}}\) are uncorrelated.
 
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Metadata
Title
An alternative data analytic approach to measure the univariate and multivariate skewness
Authors
Ravindra Khattree
Manoj Bahuguna
Publication date
06-03-2018
Publisher
Springer International Publishing
Published in
International Journal of Data Science and Analytics / Issue 1/2019
Print ISSN: 2364-415X
Electronic ISSN: 2364-4168
DOI
https://doi.org/10.1007/s41060-018-0106-1

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