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Detecting latent components in ordinal data with overdispersion by means of a mixture distribution

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  • Maria Iannario

Abstract

The paper describes a mixture distribution generated by Beta Binomial and Uniform random variables to allow for a possible overdispersion in surveys when the response of interest is an ordinal variable. This approach considers the joint presence of feeling, uncertainty and a possible dispersion sometimes present in the evaluation contexts. After a discussion of the main properties of this class of models, asymptotic likelihood methods have been applied for efficient statistical inference. The implementation on the survey on household income and wealth (SHIW) will confirm the versatility of this distribution and the usefulness to distinguish the determinants of uncertainty and overdispersion in real data. Copyright Springer Science+Business Media Dordrecht 2015

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  • Maria Iannario, 2015. "Detecting latent components in ordinal data with overdispersion by means of a mixture distribution," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(3), pages 977-987, May.
    • Handle: RePEc:spr:qualqt:v:49:y:2015:i:3:p:977-987
    DOI: 10.1007/s11135-014-0113-9
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    References listed on IDEAS

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    1. Riccardo Sartori & Andrea Ceschi, 2011. "Uncertainty and its perception: experimental study of the numeric expression of uncertainty in two decisional contexts," Quality & Quantity: International Journal of Methodology, Springer, vol. 45(1), pages 187-198, January.
    2. Ram Tripathi & Ramesh Gupta & John Gurland, 1994. "Estimation of parameters in the beta binomial model," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 46(2), pages 317-331, June.
    3. Rossi P. E & Gilula Z. & Allenby G. M, 2001. "Overcoming Scale Usage Heterogeneity: A Bayesian Hierarchical Approach," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 20-31, March.
    4. Hinde, John & Demetrio, Clarice G. B., 1998. "Overdispersion: Models and estimation," Computational Statistics & Data Analysis, Elsevier, vol. 27(2), pages 151-170, April.
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    1. Marcella Corduas & Alfonso Piscitelli, 2017. "Modeling university student satisfaction: the case of the humanities and social studies degree programs," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(2), pages 617-628, March.
    2. Evgeniy M. Ozhegov & Daria Teterina, 2018. "The Ensemble Method For Censored Demand Prediction," HSE Working papers WP BRP 200/EC/2018, National Research University Higher School of Economics.
    3. Anna Gottard & Maria Iannario & Domenico Piccolo, 2016. "Varying uncertainty in CUB models," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 10(2), pages 225-244, June.
    4. 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.
    5. Maria Iannario & Marica Manisera & Domenico Piccolo & Paola Zuccolotto, 2020. "Ordinal Data Models for No-Opinion Responses in Attitude Survey," Sociological Methods & Research, , vol. 49(1), pages 250-276, February.
    6. 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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