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Ali-Mikhail-Haq copula to detect low correlations in hierarchical clustering

F. Marta L. Di Lascio
•
Andrea Menapace
•
Roberta Pappadà
2021
  • conference object

Abstract
In this work we introduce a new dissimilarity measure based on the AliMikhail-Haq copula, motivated by the empirical issue of detecting low correlations and discriminating variables with very similar rank correlation. This issue arises from the analysis of panel data concerning the district heating demand of the Italian city Bozen-Bolzano. In the hierarchical clustering framework, we empirically investigate the features of the proposed measure and compare it with a classical dissimilarity measure based on Kendall’s rank correlation.
DOI
10.36253/978-88-5518-340-6
Archivio
http://hdl.handle.net/11368/2994383
https://fupress.com/catalogo/cladag-2021-book-of-abstracts-and-short-papers-/7254
Diritti
open access
license:creative commons
license uri:http://creativecommons.org/licenses/by/4.0/
FVG url
https://arts.units.it/bitstream/11368/2994383/2/Pappada_Ali-Mikhail-Haq copula to detect low correlations in hierarchical.pdf
Soggetti
  • Ali-Mikhail-Haq copul...

  • cluster analysi

  • dissimilarity measure...

  • low correlation

Visualizzazioni
4
Data di acquisizione
Apr 19, 2024
Vedi dettagli
google-scholar
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