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Rejoinder to the discussion of “The class of cub models: statistical foundations, inferential issues and empirical evidence”

Author

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  • Domenico Piccolo

    (University of Naples Federico II)

  • Rosaria Simone

    (University of Naples Federico II)

Abstract

The paper is the rejoinder to a series of Discussions on the class of cub models for rating data. The main topics advanced by Discussants are reviewed and debated, with focus on the most prominent issues. As a result, the trailhead of possible future research developments is outlined.

Suggested Citation

  • Domenico Piccolo & Rosaria Simone, 2019. "Rejoinder to the discussion of “The class of cub models: statistical foundations, inferential issues and empirical evidence”," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(3), pages 477-493, September.
    • Handle: RePEc:spr:stmapp:v:28:y:2019:i:3:d:10.1007_s10260-019-00479-5
    DOI: 10.1007/s10260-019-00479-5
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    References listed on IDEAS

    as
    1. 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.
    2. Ron S. Kenett & Galit Shmueli, 2014. "On information quality," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 177(1), pages 3-38, January.
    3. repec:cup:apsrev:v:98:y:2004:i:01:p:191-207_00 is not listed on IDEAS
    4. D'Elia, Angela & Piccolo, Domenico, 2005. "A mixture model for preferences data analysis," Computational Statistics & Data Analysis, Elsevier, vol. 49(3), pages 917-934, June.
    5. Viola Angelini & Danilo Cavapozzi & Luca Corazzini & Omar Paccagnella, 2014. "Do Danes and Italians Rate Life Satisfaction in the Same Way? Using Vignettes to Correct for Individual-Specific Scale Biases," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(5), pages 643-666, October.
    6. Manisera, Marica & Zuccolotto, Paola, 2014. "Modeling rating data with Nonlinear CUB models," Computational Statistics & Data Analysis, Elsevier, vol. 78(C), pages 100-118.
    7. Jacques, Julien & Biernacki, Christophe, 2018. "Model-based co-clustering for ordinal data," Computational Statistics & Data Analysis, Elsevier, vol. 123(C), pages 101-115.
    8. Martin S. Ridout, 1999. "Memory in Coal Tits: An Alternative Model," Biometrics, The International Biometric Society, vol. 55(2), pages 660-662, June.
    9. Leonardo Grilli & Maria Iannario & Domenico Piccolo & Carla Rampichini, 2014. "Latent class 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. 8(1), pages 105-119, March.
    10. E. Nardo & R. Simone, 2019. "A model-based fuzzy analysis of questionnaires," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(2), pages 187-215, June.
    11. Chstoph Bandt & Faten Shiha, 2007. "Order Patterns in Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 28(5), pages 646-665, September.
    12. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Antonio Calcagnì & Luigi Lombardi, 2022. "Modeling random and non-random decision uncertainty in ratings data: a fuzzy beta model," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 106(1), pages 145-173, March.
    2. Ribecco, Nunziata & D'Uggento, Angela Maria & Labarile, Angela, 2022. "What influences the perception of immigration in Italian adolescents? An analysis with CUB models for rating data," Socio-Economic Planning Sciences, Elsevier, vol. 82(PB).
    3. Rosaria Simone, 2021. "An accelerated EM algorithm for mixture models with uncertainty for rating data," Computational Statistics, Springer, vol. 36(1), pages 691-714, March.
    4. Beatriz Tovar & David Boto-García & José Francisco Baños Pino, 2024. "Meeting externalities: The effects of educational training on support for tourism activities," Tourism Economics, , vol. 30(3), pages 785-805, May.
    5. Ryan H. L. Ip & K. Y. K. Wu, 2024. "A mixture distribution for modelling bivariate ordinal data," Statistical Papers, Springer, vol. 65(7), pages 4453-4488, September.
    6. Corduas, Marcella, 2022. "Gender differences in the perception of inflation," Journal of Economic Psychology, Elsevier, vol. 90(C).
    7. Stefania Capecchi & Francesca Di Iorio & Nunzia Nappo, 2024. "A mixture model for self-assessed stress at work across EU 163," RIEDS - Rivista Italiana di Economia, Demografia e Statistica - The Italian Journal of Economic, Demographic and Statistical Studies, SIEDS Societa' Italiana di Economia Demografia e Statistica, vol. 78(2), pages 163-174, April-Jun.
    8. Gerhard Tutz, 2022. "Ordinal Trees and Random Forests: Score-Free Recursive Partitioning and Improved Ensembles," Journal of Classification, Springer;The Classification Society, vol. 39(2), pages 241-263, July.
    9. Rosaria Simone, 2023. "Uncertainty Diagnostics of Binomial Regression Trees for Ordered Rating Data," Journal of Classification, Springer;The Classification Society, vol. 40(1), pages 79-105, April.
    10. Roberto Colombi & Sabrina Giordano & Gerhard Tutz, 2021. "A Rating Scale Mixture Model to Account for the Tendency to Middle and Extreme Categories," Journal of Educational and Behavioral Statistics, , vol. 46(6), pages 682-716, December.
    11. Heng Xu & Nan Zhang, 2022. "From Contextualizing to Context Theorizing: Assessing Context Effects in Privacy Research," Management Science, INFORMS, vol. 68(10), pages 7383-7401, October.
    12. Cantone, Giulio Giacomo & Tomaselli, Venera, 2023. "Quasi-experimental network-based design for semantic analysis of small clusters of bi-polar online reviews," SocArXiv v7u3h, Center for Open Science.
    13. Rosaria Simone, 2022. "On finite mixtures of Discretized Beta model for ordered responses," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(3), pages 828-855, September.
    14. Manisera, Marica & Zuccolotto, Paola, 2022. "A mixture model for ordinal variables measured on semantic differential scales," Econometrics and Statistics, Elsevier, vol. 22(C), pages 98-123.
    15. Stefania Capecchi & Romina Gambacorta & Rosaria Simone & Domenico Piccolo, 2024. "Modelling cognitive response patterns to survey questions using the class of CUB models," Questioni di Economia e Finanza (Occasional Papers) 885, Bank of Italy, Economic Research and International Relations Area.

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