Open Conference Systems, 50th Scientific meeting of the Italian Statistical Society

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A dissimilarity-based splitting criterion for CUBREMOT
carmela cappelli, Rosaria Simone, Francesca Di Iorio

Last modified: 2018-05-17


CUBREMOT  (CUB REgression MOdel Trees) is a model-based approach to grow trees  for ordinal responses that relies on a class of mixture models for evaluations and preferences (CUB). The original proposal considers deviances in log-likelihood to partition observations.  In the present paper a new splitting criterion is introduced that, among the significant splitting variables, chooses the one that maximizes  a dissimilarity measure. This choice is tailored at generating  child nodes as far apart as  possible with respect to the estimated probability distributions. An application to real data on Italians' trust towards the European Parliament taken from the official survey on daily life conducted by the Italian National Institute of Statistics (ISTAT) in 2015 is presented and discussed in comparison with alternative methods.


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