References
- Beran, T. and Violato, C. (2005). Ratings of university teacher instruction: how much do student and course characteristics really matter? Assessment & Evaluation in Higher Education, 30, 593-601. https://doi.org/10.1080/02602930500260688
- Blischke, W. (1964). Estimating the parameters of mixture of binomial distributions. Journal of the American Statistical Association, 59, 510-528. https://doi.org/10.1080/01621459.1964.10482176
- Bonnini, S., Piccolo, D., Salmaso, L. and Solmi, F. (2012). Permutation inference for a class of mixture models. Communications in Statistics-Theory and Methods, 41, 2879-2895. https://doi.org/10.1080/03610926.2011.590915
- Cicia, G., Corduas, M., Giudice, T. D. and Piccolo, D. (2010). Valuing consumer preferences with the CUB model: A case study of fair trade coffee. International Journal on Food System Dynamics, 1, 82-93.
- Corduas, M. (2011). A study on university students' opinions about teaching quality: a model based approach for clustering ordinal data. In M. Attanasio & V. Capursi Jackson (Eds.), Statistical Methods for the Evaluation of University Systems. Heidelberg: Springer.
- Cekanavicius, V., Pekoz, E. A., Rollin, A. and Shwartz, M. (2009). A three-parameter binomial approximation. Available from http://arxiv.org/abs/0906.2855. https://doi.org/10.1239/jap/1261670689
- Copsey, K. and Webb, A. (2003). Bayesian gamma mixture model approach to radar target recognition. IEEE Transactions on Aerospace and Electronic Systems, 39, 1201-1217. https://doi.org/10.1109/TAES.2003.1261122
- D'Elia, A. and Piccolo, D. (2005). A mixture model for preference data analysis. Computational Statistics and Data Analysis, 49, 917-934. https://doi.org/10.1016/j.csda.2004.06.012
- Dempster, A. P., Laird, N. M. and Rubin, D. R. (1977). Maximum likelihood from incomplete data. Journal of the Royal Statistical Society B, 39, 1-38.
- Domenico, P. (2003). On the moments of a mixture of uniform and shifted binomial random variables. Quaderni di Statistica, 5, 1-20.
- Greenwald, A. G. (2002). Constructs in student ratings of instructors. In H. I. Braun, D. N. Jackson, & D. E. Wiley (Eds.), The role of constructs in psychological and educational measurement. New York:Erlbaum.
- Iannario M. (2010). On the identifiability of a mixture model for ordinal data. METRON, LXVIII, 87-94.
- Iannario M. (2012a). Preliminary estimators for a mixture model of ordinal data. Adv Data Anal Classif , 5, 163-184.
- Iannario M. (2012b). Modelling shelter choices in a class of mixture models for ordinal responses. Stat Methods Appl, 21, 1-22. https://doi.org/10.1007/s10260-011-0176-x
- Iannario, M., Manisera, M., Piccolo, D. and Zuccolotto, P. (2012). Sensory analysis in the food industry as a tool for marketing decisions. Adv Data Anal Classif , 6, 303-321. https://doi.org/10.1007/s11634-012-0120-4
- Iannario M. and Piccolo D. (2011). CUB Models: Statistical Methods and Empirical Evidence. Modern Analysis of Customer Satisfaction Surveys, Kenett R. S. and Salini S. (Eds). John Wiley and Sons: Chichester: UK.
- Johnson, N. L., Kemp, A. W. and Kotz, S. (2005). Univariate discrete distributions, 3rd ed., Wiley-Interscience, New York.
- Kenett, R. S. and Salini, S. (2011). Modern analysis of customer satisfaction surveys: comparison of models and integrated analysis. Applied Stochastic Models in Business and Industry, 27, 465-475. https://doi.org/10.1002/asmb.927
- Lee, H. J. and Oh, C. (2006). Estimation in mixture of shifted Poisson distributions with known shift parameters. Journal of the Korean Data & Information Science Society, 17, 785-794.
- Liu, Z., Almhana, J., Choulakian, V. and McGorman, R. (2006). Online EM algorithm for mixture with application to internet traffic modeling. Computational Statistics & Data Analysis, 50, 1052-1071. https://doi.org/10.1016/j.csda.2004.11.002
- McLachlan, G. J. and Krishnan, T. (2008). The EM algorithm and extensions, 2nd ed., Wiley, Hoboken, NJ.
- McLachlan, G. J. and Peel, D. (2001). Finite mixture models, John Wiley & Sons, Inc., New York.
- Oh, C. (2006). Estimation in mixture of shifted Poisson distributions. Journal of the Korean Data & Information Science Society, 17, 1209-1217.
- Oh, C. (2014). A maximum likelihood estimation method for a mixture of shifted binomial distributions. Journal of the Korean Data & Information Science Society, 25, 255-261. https://doi.org/10.7465/jkdi.2014.25.1.255
- Piccolo D. (2003). On the moments of a mixture of uniform and shifted binomial random variables. Quaderni di Statistica, 5, 85-104.
- Piccolo D. and D'Elia A. (2008). A new approach for modeling consumers' preferences. Food Quality Preference, 19, 247-259. https://doi.org/10.1016/j.foodqual.2007.07.002
- Skipper, M. (2012). A Polya approximation to the Poisson-binomial law. Journal of Apply Probability, 49, 745-757. https://doi.org/10.1239/jap/1346955331
Cited by
- Estimation of the case fatality ratio of MERS epidemics using information on patients' severity condition vol.27, pp.3, 2016, https://doi.org/10.7465/jkdi.2016.27.3.599
- Semiparametric mixture of experts with unspecified gate network vol.28, pp.3, 2015, https://doi.org/10.7465/jkdi.2017.28.3.685