Abstract
AbstractThe paper proposes a method to perform diagnostics of model-based trees for preference and evaluation data on the basis of surrogate residual analysis for ordinal data models. The discussion stems from the introduction of binomial regression trees and discusses how to perform local diagnostics of misspecification against alternative model extensions within the framework of mixture models with uncertainty. Three case studies concerning customer satisfaction and perceived trust for information sources illustrate usefulness and versatile applicative extent of the proposal.
Funder
Università degli Studi di Napoli Federico II
Publisher
Springer Science and Business Media LLC
Subject
Library and Information Sciences,Statistics, Probability and Uncertainty,Psychology (miscellaneous),Mathematics (miscellaneous)
Reference37 articles.
1. Allik, J. (2014). A mixed-binomial model for Likert-type personality measures. Frontiers in psychology, vol. 5.
2. Ballante, E., Figini, S., & Uberti, P. (2022). A new approach in model selection for ordinal target variables. Computational Statistics, 37(1), 43–56.
3. Banchelli, F. (2019). Flexible model-based trees for count data. In G. C. Porzio, F. Greselin, & S. Balzano (Eds.) Cladag 2019: Book of short papers. ISBN: 978-88-8317-108-6: Edizioni Università di Cassino, pp. 63–66.
4. Bloemer, J., de Ruyter, K., & Wetzels, M. (1999). Linking perceived service quality and service loyalty: A multi-dimensional perspective. European Journal of Marketing, 33(11-12), 1082–1106.
5. Burnham, K. P., & Anderson, D. R. (2003). Model selection and multimodel inference: A practical information-theoretic approach, 2nd ed. New York: Springer.