Regression for ordinal variables without underlying continuous variables

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. 2005 Elsevier Inc. All rights reserved. 0020-0255/$ see front matter 2005 Elsevier Inc. All rights reserved. doi:10.1016/j.ins.2005.07.007 * Corresponding author. Tel.: +34 93580 9570; fax: +34 93580 9661. E-mail addresses: [email protected] (V. Torra), [email protected] (J. Domingo-Ferrer), [email protected] (J.M. Mateo-Sanz), [email protected] (M. Ng). 466 V. Torra et al. / Information Sciences 176 (2006) 465–474