Shape publications

  • R. Gambacorta, M. Iannario, R. Valliant (2014). Design-based inference in a mixture model for ordinal variables for a stage stratifies design. Australian & New Zealand Journal of Statistics, 56(2), 125-143.
  • M. Manisera, P. Zuccolotto (2014) Modelling “don’t know” responses in rating scales. Pattern Recognition Letters, 45, 226-234
  • M. Manisera, P. Zuccolotto (2014) Modelling rating data with Nonlinear CUB models. Computational Statistics and Data Analysis, 78, 100-118
  • M. Manisera, P. Zuccolotto (2013) Nonlinear CUB models: some stylized facts. QdS – Journal of Methodological and Applied Statistics, 15, 1-20
  • M. Iannario, D. Piccolo (2014) A theorem on CUB models for rank data. Statistics and Probability Letters, 91, 27-31
  • Capecchi, D. Piccolo (2014) Modelling the Latent components of Personal Happiness, in: Perna, M.Sibillo (eds.), Mathematical and Statistical Methods for Actuarial Sciences and Finance, Springer-Verlag, Berlin, pp.49-52
  • Pennoni, F., Vittadini, G. (2013). Two competing models for ordinal longitudinal data with time-varying latent effects: an application to evaluate hospital efficiency. QdS – Journal of Methodological and Applied Statistics, 15, 53-68.
  • R. Colombi, S. Giordano (2013). Nested continuation logit models for ordinal variables. QdS – Journal of Methodological and Applied Statistics, 15, 19-32
  • M. Iannario (2014). Detecting latent components in ordinal data with overdispersion by means of a mixture distribution. QUALITY  AND QUANTITY, doi:10.1007/s11135-014-0113-9
  • Tutz G, Schneider M, Iannario M, Piccolo D (2014) Mixture Models for Ordinal Responses to Account for Uncertainty of Choice. Technical Report Number 175, Department of Statistics, University of Munich, http://www.stat.uni-muenchen.de
  • Capecchi, S., Ghiselli, S. (2014). Modelling Job Satisfaction of Italian Graduates, in Studies in Theoretical and Applied Statistics, pp. 37-48, Springer, Berlin. doi 10.1007/10104_2014__7
  • M. Iannario (2014). Testing overdispersion in CUBE models. Communications in Statistics. Simulation and Computation, forthcoming
  • Piccolo, D. (2014) Inferential issues on CUBE models with covariates. Communications in Statistics. Theory and Methods, 44, forthcoming
  • Capecchi, S., Piccolo, D. (2014). Investigating the determinants of job satisfaction of Italian graduates: a model-based approach, Journal of Applied Statistics., forthcoming
  • Corduas, M. (2014). Analyzing bivariate ordinal data with CUB margins. Statistical Modelling. doi: 10.1177/1471082X14558770