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Inferential Issues on CUBE Models with Covariates

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  • Domenico Piccolo

Abstract

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 paper concerns the analytical derivation of the observed variance–covariance matrix of this model, a necessary step for the asymptotic inference about estimated parameters and model validation. We emphasize some computational aspects of the procedure and discuss the usefulness of the approach on a real case study.

Suggested Citation

  • Domenico Piccolo, 2015. "Inferential Issues on CUBE Models with Covariates," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 44(23), pages 5023-5036, December.
    • Handle: RePEc:taf:lstaxx:v:44:y:2015:i:23:p:5023-5036
    DOI: 10.1080/03610926.2013.821487
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    Cited by:

    1. Domenico Piccolo & Rosaria Simone, 2019. "The class of cub models: statistical foundations, inferential issues and empirical evidence," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(3), pages 389-435, September.
    2. Capecchi, Stefania & Amato, Mario & Sodano, Valeria & Verneau, Fabio, 2019. "Understanding beliefs and concerns towards palm oil: Empirical evidence and policy implications," Food Policy, Elsevier, vol. 89(C).
    3. Stefania Capecchi & Maria Iannario & Rosaria Simone, 2018. "Well-Being and Relational Goods: A Model-Based Approach to Detect Significant Relationships," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 135(2), pages 729-750, January.
    4. Stefania Capecchi & Rosaria Simone, 2019. "A Proposal for a Model-Based Composite Indicator: Experience on Perceived Discrimination in Europe," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 141(1), pages 95-110, January.
    5. Maria Iannario & Domenico Piccolo, 2016. "A comprehensive framework of regression models for ordinal data," METRON, Springer;Sapienza Università di Roma, vol. 74(2), pages 233-252, August.

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