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On describing multivariate skewed distributions : a directional approach
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Ferreira, Jose T. A. S. and Steel, Mark F. J. (2006) On describing multivariate skewed distributions : a directional approach. Canadian Journal of Statistics, Volume 34 (Number 3). pp. 411-429. doi:10.1002/cjs.5550340304 ISSN 0319-5724.
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Official URL: http://dx.doi.org/10.1002/cjs.5550340304
Abstract
Most multivariate measures of skewness in the literature measure the overall skewness of a distribution. These measures were designed for testing the hypothesis of distributional symmetry; their relevance for describing skewed distributions is less obvious. In this article, the authors consider the problem of characterizing the skewness of multivariate distributions. They define directional skewness as the skewness along a direction and analyze two parametric classes of skewed distributions using measures based on directional skewness. The analysis brings further insight into the classes, allowing for a more informed selection of classes of distributions for particular applications. The authors use the concept of directional skewness twice in the context of Bayesian linear regression under skewed error: first in the elicitation of a prior on the parameters of the error distribution, and then in the analysis of the skewness of the posterior distribution of the regression residuals.
Item Type: | Journal Article | ||||
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Subjects: | Q Science > QA Mathematics | ||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | ||||
Journal or Publication Title: | Canadian Journal of Statistics | ||||
Publisher: | Wiley-Blackwell Publishing, Inc. | ||||
ISSN: | 0319-5724 | ||||
Official Date: | September 2006 | ||||
Dates: |
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Volume: | Volume 34 | ||||
Number: | Number 3 | ||||
Number of Pages: | 19 | ||||
Page Range: | pp. 411-429 | ||||
DOI: | 10.1002/cjs.5550340304 | ||||
Status: | Peer Reviewed | ||||
Publication Status: | Published | ||||
Access rights to Published version: | Restricted or Subscription Access |
Data sourced from Thomson Reuters' Web of Knowledge
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