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  • 标题:Neural modelling of ranking data with an application to stated preference data
  • 本地全文:下载
  • 作者:Catherine Krier ; Michel Mouchart ; Abderrahim Oulhaj
  • 期刊名称:Statistica
  • 印刷版ISSN:1973-2201
  • 出版年度:2012
  • 卷号:72
  • 期号:3
  • 页码:255-269
  • DOI:10.6092/issn.1973-2201/3646
  • 语种:English
  • 出版社:Dep. of Statistical Sciences "Paolo Fortunati", Università di Bologna
  • 摘要: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 perceptron-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 pinpointed 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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