Ordinal models can be seen as being composed from simpler, in particular binary models.
This view on ordinal models allows to derive a taxonomy of models that includes basic …

This chapter is devoted to a new class of statistical models, called CUB models, introduced
for the purpose of interpreting and fitting ordinal responses. After a brief discussion of …

In this article we introduce a probability distribution generated by a mixture of discrete
random variables to capture uncertainty, feeling, and overdispersion, possibly present in …

We propose a probabilistic framework for the treatment of “don't know” responses in surveys
aimed at investigating human perceptions through expressed ratings. The rationale behind …

Ordinal measurements as ratings, preference and evaluation data are very common in
applied disciplines, and their analysis requires a proper modelling approach for …

A general statistical model for ordinal or rating data, which includes some existing
approaches as special cases, is proposed. The focus is on the CUB models and a new class …

In CUB models the uncertainty of choice is explicitly modelled as a Combination of discrete
Uniform and shifted Binomial random variables. The basic concept to model the response as …

Literature on the models for ordinal variables grew very fast in the last decades and several
proposals have been advanced when ordered data are expression of ratings, preferences …

We introduce cube models with covariates, a class of discrete mixture distributions able to
take uncertainty and overdispersion of ordinal data into account. The main result of the …

In this paper, a new discrete statistical model for ordered categorical data is proposed via
fixed-point discretization of a beta latent variable. The resulting discretized beta distribution …