Detailed Information on Publication Record

GÓRECKI, Jan, Marius HOFERT and Martin HOLEŇA. Kendall's tau and agglomerative clustering for structure determination of hierarchical Archimedean copulas. Dependence Modeling. 2017, vol. 5, No 1, p. 75-87. ISSN 2300-2298.

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Basic information
Original name	Kendall's tau and agglomerative clustering for structure determination of hierarchical Archimedean copulas
Authors	GÓRECKI, Jan (203 Czech Republic, belonging to the institution), Marius HOFERT (276 Germany) and Martin HOLEŇA (203 Czech Republic).
Edition	Dependence Modeling, 2017, 2300-2298.

Other information
Original language	English
Type of outcome	Article in a journal
Field of Study	10100 1.1 Mathematics
Country of publisher	Poland
Confidentiality degree	is not subject to a state or trade secret
WWW	URL
RIV identification code	RIV/47813059:19520/17:00010816
Organization unit	School of Business Administration in Karvina
Keywords in English	structure determination; agglomerative clustering; Kendall's tau; Archimedean copula
Changed by	Changed by: RNDr. Daniel Jakubík, učo 139797. Changed: 7/2/2020 10:57.

Abstract
Several successful approaches to structure determination of hierarchical Archimedean copulas (HACs) proposed in the literature rely on agglomerative clustering and Kendall's correlation coefficient. However, there has not been presented any theoretical proof justifying such approaches. This work fills this gap and introduces a theorem showing that, given the matrix of the pairwise Kendall correlation coefficients corresponding to a HAC, its structure can be recovered by an agglomerative clustering technique.

Abstract

Several successful approaches to structure determination of hierarchical Archimedean copulas (HACs) proposed in the literature rely on agglomerative clustering and Kendall's correlation coefficient. However, there has not been presented any theoretical proof justifying such approaches. This work fills this gap and introduces a theorem showing that, given the matrix of the pairwise Kendall correlation coefficients corresponding to a HAC, its structure can be recovered by an agglomerative clustering technique.