A new approach for modelling consumers’ preferences

Piccolo, Domenico; D’Elia, Angela

doi:10.1016/j.foodqual.2007.07.002

Cited by 75 publications

(49 citation statements)

References 22 publications

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“…Similar considerations may be pursued by inserting a varying uncertainty in the gecub structure and this extension does not require further parameters to be estimated. -In some circumstances, as in sensometric studies, it is convenient to introduce objects' covariates (Piccolo and D'Elia 2008) in the link of the parameters since consumers' preferences are undoubtedly conditioned by the sensory characteristics of the items under scrutiny (food or beverage, for instance). This proposal may be usefully applied to gecub models.…”

Section: Discussionmentioning

confidence: 99%

A generalized framework for modelling ordinal data

Iannario

Piccolo

2015

Stat Methods Appl

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In several applied disciplines, as Economics, Marketing, Business, Sociology, Psychology, Political science, Environmental research and Medicine, it is common to collect data in the form of ordered categorical observations. In this paper, we introduce a class of models based on mixtures of discrete random variables in order to specify a general framework for the statistical analysis of this kind of data. The structure of these models allows the interpretation of the final response as related to feeling, uncertainty and a possible shelter option and the expression of the relationship among these components and subjects' covariates. Such a model may be effectively estimated by maximum likelihood methods leading to asymptotically efficient inference. We present a simulation experiment and discuss a real case study to check the consistency and the usefulness of the approach. Some final considerations conclude the paper.

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Section: Discussionmentioning

confidence: 99%

A generalized framework for modelling ordinal data

Iannario

Piccolo

2015

Stat Methods Appl

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“…Moreover, the influence of external factors, w, in the final judgement can be taken into consideration by relating the model parameters x and/or x to significant covariates describing the raters, by means of a logistic link function [30]. For this aim, two further relations are added:…”

Section: The Marginal Distributionsmentioning

confidence: 99%

“…The data originates from a large survey which was carried out during the Euro-salmon project and which involved consumers' panels from various European countries [4,7]. This data set was analysed by Piccolo and D'Elia [30] who estimated the univariate CUB models for the 30 products and introduced both product and subject features as covariates affecting the CUB parameters.…”

Section: An Empirical Applicationmentioning

confidence: 99%

Modelling Correlated Consumer Preferences

Corduas

2014

Advances in Latent Variables

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The CUB model is a mixture distribution recently proposed in literature for modelling ordinal data. The CUB parameters may be related to explanatory variables describing the raters or the object of evaluation. Although various methodological aspects of this class of models have been investigated, the problem of multivariate ordinal data representation is still open. In this article the Plackett distribution is used in order to construct a bivariate distribution from CUB margins. Furthermore, the model is extended so that the effect of rater characteristics on their stated preferences is included.

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“…These models have been applied in several contexts such as Marketing Piccolo 2010a, 2011), Medicine (D'Elia 2008), Sensometrics (Piccolo and D'Elia 2008;Piccolo and Iannario 2010), Urban emergencies (Iannario 2007), Linguistic analyses (Balirano andCorduas 2008;Caliendo and Iannario 2009), Risk perception (Cerchiello et al 2010;Corduas 2008) and evaluation of subjective probabilities (Iannario and Piccolo 2010b). Although these applications are relevant for interpretative objectives, in this paper we only consider cub models without covariates.…”

Section: A Class Of Models For Ordinal Datamentioning

confidence: 99%

Modelling shelter choices in a class of mixture models for ordinal responses

Iannario

2011

Stat Methods Appl

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In rating surveys, people are requested to evaluate objects, items, services, and so on, by choosing among a list of ordered categories. In some circumstances, it may happen that a subset of respondents selects a specific option just to simplify a more demanding choice. In this context, we generalize a class of ordinal data models (called CUB and proven effective for fitting and interpretation), for taking the possible presence of a shelter choice into account. After the discussion of interpretative and inferential issues, the usefulness of the approach is checked against real case studies and by means of a simulation experiment. Some final remarks end the paper

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A new approach for modelling consumers’ preferences

Cited by 75 publications

References 22 publications

A generalized framework for modelling ordinal data

A generalized framework for modelling ordinal data

Modelling Correlated Consumer Preferences

Modelling shelter choices in a class of mixture models for ordinal responses

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