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BEGIN:VEVENT
DTSTAMP:20260409T200823Z
DTSTART;VALUE=DATE-TIME:20210615T160000
DTEND;VALUE=DATE-TIME:20210615T170000
SUMMARY:Zeeshan Ali: A new measure of distance for compositional data
TZID:Europe/London
UID:20210615-8a1785d779ae97850179c37044561ede@warwick.ac.uk
CREATED:20211005T124712Z
DESCRIPTION:Compositional data refer to non-negative vectors whose sum is
  constant and convey relative information. Aitchison (1992) developed a 
 set of criteria for a suitable measure of distance or dissimilarity for 
 compositional data. Based on these criteria\, Aitchison (1992) introduce
 d a distance measure for compositional data\, known as Aitchison distanc
 e. The other published measure\, to the best of our knowledge\, that sat
 isfies all the criteria is the compositional Kullback-Leibler (C-KL) dis
 similarity. This paper introduces a new distance measure for composition
 al data. The proposed distance measure is capable of handling the zero v
 alues in the compositions\, unlike Aitchison distance and C-KL dissimila
 rity. Furthermore\, this paper shows that two of the criteria (perturbat
 ion invariance and subcompositional dominance) set out by Aitchison (199
 2) are too stringent for some applications involving compositional data.
  Two numerical examples from political science are included as counterex
 amples of perturbation invariance and subcompositional dominance criteri
 a. It has shown that the proposed measure\, which does not satisfy both 
 perturbation invariance and subcompositional dominance criteria\, perfor
 m better than Aitchison distance and C-KL dissimilarity.
LOCATION:
CATEGORIES:
LAST-MODIFIED:20211005T124712Z
ORGANIZER;CN="Timothée Stumpf-Fétizon":
END:VEVENT
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