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Discussion of ‘The class of CUB models: statistical foundations, inferential issues and empirical evidence’ by Domenico Piccolo and Rosaria Simone

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Abstract

In this note we briefly discuss the structure of CUB models and their interpretation. Furthermore, we elaborate some issues related to the comparison of CUB models with mainstream approaches, focusing on generalized linear models for univariate ordinal responses and classical latent variable models for multivariate ordinal responses.

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References

  • Bock RD (1972) Estimating item parameters and latent ability when responses are scored in two or more nominal categories. Psychometrika 37:29–51

    Article  MATH  Google Scholar 

  • Iannario M, Piccolo D (2016) A generalized framework for modelling ordinal data. Stat Methods Appl 25:163–189

    Article  MathSciNet  MATH  Google Scholar 

  • Lazarsfeld PF, Henry NW (1968) Latent structure analysis. Houghton-Mifflin, Boston

    MATH  Google Scholar 

  • Manisera M, Zuccolotto P (2014) Modeling rating data with nonlinear CUB models. Comput Stat Data Anal 78:100–118

    Article  MathSciNet  MATH  Google Scholar 

  • Masters GN (1982) A rasch model for partial credit scoring. Psychometrika 47(2):149–174

    Article  MATH  Google Scholar 

  • McLachlan G, Peel GJ (2000) Finite mixture models. Wiley, New York

    Book  MATH  Google Scholar 

  • Oberski DL, Vermunt JK (2015) The relationship between CUB and loglinear models with latent variables. Electron J Appl Stat Anal 8(3):374–383

    MathSciNet  Google Scholar 

  • Piccolo D, Simone R (2019) The class of CUB models: Statistical foundations, inferential issues and empirical evidence. Stat Methods Appl. https://doi.org/10.1007/s10260-019-00461-1

    Google Scholar 

  • Piccolo D, Simone R, Iannario M (2018) Cumulative and CUB models for rating data: a comparative analysis. Int Stat Rev, pp 1–30. https://doi.org/10.1111/insr.12282

  • Skrondal A, Rabe-Hesketh S (2004) Generalized latent variable modeling: multilevel, longitudinal and structural equation models. Chapman & Hall, Boca Raton

    Book  MATH  Google Scholar 

  • Tutz G (2012) Regression for Categorical Data. Cambridge University Press, Cambridge

    MATH  Google Scholar 

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Authors and Affiliations

  1. Department of Statistics, Computer Science, Applications ‘G. Parenti’, University of Florence, Florence, Italy

    Leonardo Grilli & Carla Rampichini

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  1. Leonardo Grilli
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  2. Carla Rampichini
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Correspondence to Carla Rampichini.

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Grilli, L., Rampichini, C. Discussion of ‘The class of CUB models: statistical foundations, inferential issues and empirical evidence’ by Domenico Piccolo and Rosaria Simone. Stat Methods Appl 28, 459–463 (2019). https://doi.org/10.1007/s10260-019-00466-w

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  • DOI: https://doi.org/10.1007/s10260-019-00466-w

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