The class of cub models: statistical foundations, inferential issues and empirical evidence

This paper discusses a general framework for the analysis of rating and preference data that is rooted on a class of mixtures of discrete random variables. These models have been extensively studied and applied in the last 15 years thanks to a flexible and parsimonious parametrization of data genera...

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Vydáno v:Statistical methods & applications Ročník 28; číslo 3; s. 389 - 435
Hlavní autoři: Piccolo, Domenico, Simone, Rosaria
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2019
Springer Nature B.V
Témata:
Chemistry and Earth Sciences
Computer Science
Economics
Empirical analysis
Finance
Health Sciences
Humanities
Insurance
Law
Management
Mathematics and Statistics
Medicine
Original Paper
Parameterization
Performance prediction
Physics
Random variables
Statistical models
Statistical Theory and Methods
Statistics
Statistics for Business
Statistics for Engineering
Statistics for Life Sciences
Statistics for Social Sciences
Uncertainty
models
Explicative power
Predictability
Data generating process
Ordinal data
Ratings
ISSN:1618-2510, 1613-981X
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Shrnutí:This paper discusses a general framework for the analysis of rating and preference data that is rooted on a class of mixtures of discrete random variables. These models have been extensively studied and applied in the last 15 years thanks to a flexible and parsimonious parametrization of data generating process and to prompt interpretation of results. The approach considers the final response as the combination of feeling and uncertainty, by allowing for finer model specifications to include refuge options, response styles and possible overdispersion, also in relation to subjects’ and objects’ covariates. The article establishes the state of art of the research inherent to this paradigm, in terms of methodology, inferential procedures and fitting measures, by emphasizing capabilities and limitations yet establishing new findings. In particular, explicative power and predictive performances of cub statistical models for ordinal data are examined and new topics that could boost and support the modelling of uncertainty in this framework are provided. Possible developments are outlined throughout the whole presentation and final comments conclude the paper.
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ISSN:1618-2510
1613-981X
DOI:10.1007/s10260-019-00461-1