Year: 2026
Type: article
Abstract: We propose a latent class model for ordinal data with CUB (combination of discrete uniform and shifted binomial) distributions in the case of multilevel structures of the data. The CUB model is a powerful approach to the analysis of ordinal data, where the elicitation process is thought to be governed by a feeling parameter and an uncertainty parameter. Ordinal data are common across different research fields and may present a multilevel structure with units nested within groups. The model we present extends the framework of multivariate CUB models for model-based clustering to multilevel data, either hierarchical or cross-classified. Numerical experiments on simulated data highlight the added value of assuming a CUB model to account for ordinal information; the procedure’s interest is also shown through a real data application.
Source: Journal of Classification
Authors: Nicola Piras, Silvia Columbu, Julien Jacques
Institutions: University of Cagliari, Université Lumière Lyon 2
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,290
Funder: Fondazione di Sardegna
OpenAlex ID: W7118286359