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 loglikelihood 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 to 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.
A dissimilarity-based splitting criterion for CUBREMOT / Cappelli, Carmela; Simone, Rosaria; DI IORIO, Francesca. - (2018).
A dissimilarity-based splitting criterion for CUBREMOT
Carmela Cappelli
;Rosaria Simone;Francesca Di Iorio
2018
Abstract
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 loglikelihood 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 to 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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
