Uncertainty Diagnostics of Binomial Regression Trees for Ordered Rating Data

Rosaria Simone ()
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Rosaria Simone: University of Naples Federico II

Journal of Classification, 2023, vol. 40, issue 1, No 5, 79-105

Abstract: Abstract The 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.

Keywords: Ordered data; Model-based trees; Binomial regression; Surrogate Residuals; Mixture models with uncertainty (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s00357-022-09429-5

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