Regression for ordinal variables without underlying continuous variables

Vicenç Torra*, Josep Domingo-Ferrer, Josep M. Mateo-Sanz, Kwok Po NG

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

51 Citations (Scopus)


Several techniques exist nowadays for continuous (i.e. numerical) data analysis and modeling. However, although part of the information gathered by companies, statistical offices and other institutions is numerical, a large part of it is represented using categorical variables in ordinal or nominal scales. Techniques for model building on categorical data are required to take advantage of such a wealth of information. In this paper, current approaches to regression for ordinal data are reviewed and a new proposal is described which has the advantage of not assuming any latent continuous variable underlying the dependent ordinal variable. Estimation in the new approach can be implemented using genetic algorithms. An artificial example is presented to illustrate the feasibility of the proposal.

Original languageEnglish
Pages (from-to)465-474
Number of pages10
JournalInformation Sciences
Issue number4
Publication statusPublished - 22 Feb 2006

Scopus Subject Areas

  • Software
  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computer Science Applications
  • Information Systems and Management
  • Artificial Intelligence

User-Defined Keywords

  • Categorical variables
  • Linear models
  • Ordinal scales
  • Regression models


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