The ultrametric correlation matrix for modelling hierarchical latent concepts

Carlo Cavicchia (), Maurizio Vichi () and Giorgia Zaccaria ()
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Carlo Cavicchia: University of Rome La Sapienza
Maurizio Vichi: University of Rome La Sapienza
Giorgia Zaccaria: University of Rome La Sapienza

Advances in Data Analysis and Classification, 2020, vol. 14, issue 4, No 7, 837-853

Abstract: Abstract Many relevant multidimensional phenomena are defined by nested latent concepts, which can be represented by a tree-structure supposing a hierarchical relationship among manifest variables. The root of the tree is a general concept which includes more specific ones. The aim of the paper is to reconstruct an observed data correlation matrix of manifest variables through an ultrametric correlation matrix which is able to pinpoint the hierarchical nature of the phenomenon under study. With this scope, we introduce a novel model which detects consistent latent concepts and their relationships starting from the observed correlation matrix.

Keywords: Ultrametric correlation matrix; Hierarchical latent concepts; Partition of variables; Hierarchical factor models; Higher-order models; 62H25 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s11634-020-00400-z

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