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Advances in Data Analysis and Classification
Erschienen in: Advances in Data Analysis and Classification 2/2016

01.06.2016 | Regular Article

Varying uncertainty in CUB models

verfasst von: Anna Gottard, Maria Iannario, Domenico Piccolo

Erschienen in: Advances in Data Analysis and Classification | Ausgabe 2/2016

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Abstract

This paper presents a generalization of a mixture model used for the analysis of ratings and preferences by introducing a varying uncertainty component. According to the standard mixture model, called CUB model, the response probabilities are defined as a convex combination of shifted Binomial and discrete Uniform random variables. Our proposal introduces uncertainty distributions with different shapes, which could capture response style and indecision of respondents with greater effectiveness. Since we consider several alternative specifications that are nonnested, we suggest the implementation of a Vuong test for choosing among them. In this regard, some simulation experiments and real case studies confirm the usefulness of the approach.
Vorheriger Artikel Power analysis for the bootstrap likelihood ratio test for the number of classes in latent class models
Nächster Artikel Item selection by latent class-based methods: an application to nursing home evaluation

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Metadaten
Titel
Varying uncertainty in CUB models
verfasst von
Anna Gottard
Maria Iannario
Domenico Piccolo
Publikationsdatum
01.06.2016
Verlag
Springer Berlin Heidelberg
Erschienen in
Advances in Data Analysis and Classification / Ausgabe 2/2016
Print ISSN: 1862-5347
Elektronische ISSN: 1862-5355
DOI
https://doi.org/10.1007/s11634-016-0235-0

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