AI Article Synopsis
- Triadic distance models help analyze relationships between triples of objects, typically assuming three-way symmetry.
- The current study challenges this assumption by presenting models that accommodate asymmetry in three-way proximity data, allowing for a nuanced representation.
- An iterative majorization algorithm is introduced to calculate the coordinates and illustrate asymmetry, demonstrated through a case study of longitudinal categorical data.
Article Abstract
Triadic distance models can be used to analyse proximity data defined on triples of objects. Three-way symmetry is a common assumption for triadic distance models. In the present study three-way symmetry is not assumed. Triadic distance models are presented for the analysis of asymmetric three-way proximity data that result in a simultaneous representation of symmetry and asymmetry in a low-dimensional configuration. An iterative majorization algorithm is developed for obtaining the coordinates and the representation of the asymmetry. The models are illustrated by an example using longitudinal categorical data.
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| http://dx.doi.org/10.1348/000711000159204 | DOI Listing |
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