Neural modelling of ranking data with an application to stated preference data
(2012) Statistica — Vol. 72, n° 3, p. 255-269 (2012)
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- Abstract
- Although neural networks are commonly encountered to solve classification problems, ranking data present specificities which require adapting the model Based on a latent utility function defined on the characteristics of the objects to be ranked, the approach suggested in this paper leads to a perception-based algorithm for a highly non linear model. Data on stated preferences obtained through a survey by face-to-face interviews, in the field of freight transport, are used to illustrate the method. Numerical difficulties are pin-pointed and a Pocket type algorithm is shown to provide an efficient heuristic to minimize the discrete error criterion. A substantial merit of this approach is to provide a workable estimation of contextually interpretable parameters along with a statistical evaluation of the goodness of fit.
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Citations
Krier, C., Mouchart, M., & Oulhaj, A. (2012). Neural modelling of ranking data with an application to stated preference data. Statistica, 72(3), 255-269. https://hdl.handle.net/2078.5/207098 (Original work published 2012)