Uncertainty Diagnostics of Binomial Regression Trees for Ordered Rating Data

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.