Abstract
A new approach for modelling discrete choices in rating or ranking problems is represented by a class of mixture models with covariates (Combination of Uniform and shifted Binomial distributions, CUB models), proposed by Piccolo (2003, Quaderni di Statistica, 5, 85–104), D’Elia & Piccolo (2005, Computational Statistics & Data Analysis, 49, 917–934), Piccolo (2006, Quaderni di Statistica, 8, 33–78) and Iannario (2010, Metron, LXVIII, 87–94). In case of a univariate response, a permutation solution to test for covariates effects has been discussed in Bonnini et al. (2012, Communication in Statistics: Theory and Methods), together with parametric inference. We propose an extension of this nonparametric test to deal with the multivariate case. The good performances of the method are showed trough a simulation study and the procedure is applied to real data regarding the evaluation of the Ski School of Sesto Pusteria (Italy).
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Acknowledgements
Authors wish to thank the University of Padova (CPDA092350/09) and the Italian Ministry for University and Research MIUR project PRIN2008 -CUP number C91J10000000001 (2008WKHJPK/002) for providing the financial support for this research.
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Bonnini, S., Salmaso, L., Solmi, F. (2013). Nonparametric Multivariate Inference Via Permutation Tests for CUB Models. In: Giudici, P., Ingrassia, S., Vichi, M. (eds) Statistical Models for Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00032-9_6
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DOI: https://doi.org/10.1007/978-3-319-00032-9_6
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