An association model for bivariate data with application to the analysis of university students' success

References

• F. Ambrogi, E. Biganzoli, and P. Boracchi, Estimating crude cumulative incidences through multinomial logit regression on discrete cause-specific hazards, Comput. Statist. Data Anal. 53(7) (2009), pp. 2767–2779. doi: 10.1016/j.csda.2009.01.001
   Web of Science ®Google Scholar
• M. Attanasio, G. Boscaino, V. Capursi, and A. Plaia, May the students career performance helpful in predicting an increase in universities income? In Statistical Models for Data Analysis, P. Giudici, S. Ingrassia, and M. Vichi, eds., Series in Studies in Classification, Data Analysis, and Knowledge Organization, Springer International Publishing, Switzerland, 2013, pp. 9–16.
   Google Scholar
• R. Bustami, E. Lesaffre, G. Molenberghs, R. Loos, M. Danckaerts, and R. Vlietinck, Modelling bivariate ordinal responses smoothly with examples from ophthalmology and genetics, Stat. Med. 20 (2001), pp. 1825–1842. doi: 10.1002/sim.793
   PubMed Web of Science ®Google Scholar
• V. Capursi and L. Librizzi, La qualitá della didattica: indicatori semplici o composti? In Dottor Divago: Discernere, Valutare e Governare la nuova Universitá, V. Capursi and G. Ghellini, eds., Franco Angeli, Milano, 2008, pp. 127–134.
   Google Scholar
• J.R. Dale, Global cross-ratio models for bivariate, discrete, ordered responses, Biometrics 42(4) (1986), pp. 909–917. doi: 10.2307/2530704
   PubMed Web of Science ®Google Scholar
• European Communities: ECTS User's Guide. Luxembourg: Office for official publications of the European Communities, 2009.
   Google Scholar
• P.H.C. Eilers and B.D. Marx, Flexible smoothing with B-splines and penalties, Statist. Sci. 11 (1996), pp. 89–121. doi: 10.1214/ss/1038425655
   Web of Science ®Google Scholar
• M. Enea, Penalized inference in multivariate ordered logistic models: Theory and applications, Ph.D. thesis in statistics and quantitative finance, University of Palermo, 2010.
   Google Scholar
• M. Enea, Penalized inference in multivariate ordered logistic models: Theory and applications, Book of short papers of Ph.D. theses in Statistics and Applications, Cleup, Padova, 2011.
   Google Scholar
• S. Fasola, Laurea o abbandono: il buongiorno si vede dal mattino? Master thesis, University of Palermo, Palermo, 2011.
   Google Scholar
• Y. Foucher, M. Giral, J.P. Soulillou, and J.P. Daures, A semi-Markov model for multistate and interval-censored data with multiple terminal events. Application in renal transplantation, Stat. Med. 2 (2007), pp. 5381–5393. doi: 10.1002/sim.3100
   Web of Science ®Google Scholar
• G.F.V. Glonek and P. McCullagh, Multivariate logistic models, J. R. Stat. Soc. Ser. B Stat. Methodol. 57 (1995), pp. 533–546.
   Web of Science ®Google Scholar
• L.A. Goodman, Simple models for the analysis of association in cross-classifications having ordered categories, J. Amer. Statist. Assoc. 74(367) (1979), pp. 537–552. doi: 10.1080/01621459.1979.10481650
   Web of Science ®Google Scholar
• E. Gori and M. Montagni, Random effects models for event data. Evaluating effectiveness of universities through the analysis of students careers, Multilevel Model. Newslett. 10(1) (1997), ESRC, England. Available at http://www.bristol.ac.uk/media-library/sites/cmm/migrated/documents/new10-1.pdf.
   Google Scholar
• R.J. Gray, Spline-based tests in survival analysis, Biometrics 50 (1994), pp. 640–652. doi: 10.2307/2532779
   PubMed Web of Science ®Google Scholar
• L. Grilli and C. Rampichini, A multilevel multinomial logit model for the analysis of graduates' skills, Statist. Methods Appl. 16(3) (2007), pp. 381–393. doi: 10.1007/s10260-006-0039-z
   Web of Science ®Google Scholar
• J. Janssen and R. Manca, Applied Semi-Markov Processes, Springer, New York, 2006.
   Google Scholar
• K. Lapp, G. Molenberghs, and E. Lesaffre, Models for the association between ordinal variables, Comput. Statist. Data Anal. 28 (1998), pp. 387–411. doi: 10.1016/S0167-9473(98)90149-9
   Web of Science ®Google Scholar
• P. McCullagh and J.A. Nelder, Generalized Linear Models, Chapman & Hall, London, 1989.
   Google Scholar
• F. Mealli and S. Pudney, Specification tests for random-effects transition models: An application to a model of the British youth training scheme, Lifetime Data Anal. 5 (1999), pp. 213–237. doi: 10.1023/A:1009667930622
   PubMed Web of Science ®Google Scholar
• G. Molenberghs and E. Lesaffre, Marginal modeling of correlated ordinal data using a multivariate Plackett distribution, J. Amer. Statist. Assoc. 89 (1994), pp. 633–644. doi: 10.1080/01621459.1994.10476788
   Web of Science ®Google Scholar
• R.L. Plackett, A class of bivariate distributions, J. Amer. Statist. Assoc. 60 (1965), pp. 516–522. doi: 10.1080/01621459.1965.10480807
   Web of Science ®Google Scholar
• R. Robinson, Pathways to completion: Patterns of progression through a university degree, Higher Educ. 47 (2004), pp. 1–20. doi: 10.1023/B:HIGH.0000009803.70418.9c
   Google Scholar
• A. Schizzerotto and F. Denti, Lost and late – The experience of abandonment and irregularity in the studies in five generations of enrolled at the University of Milan-Bicocca, University Milan-Bicocca, 2005. Available at http://nucleo.unimib.it/nucleoHTM/Abbandoni_2004.pdf.
   Google Scholar
• I. Sulis and V. Capursi, Building up adjusted indicators of students' evaluation of university courses using generalized item response models, J. Appl. Stat. 40(1) (2013), pp. 88–102. doi: 10.1080/02664763.2012.734796
   Web of Science ®Google Scholar
• G. Tutz, Generalized semiparametrically structured ordinal models, Biometrics 59(2) (2003), pp. 263–273. doi: 10.1111/1541-0420.00033
   PubMed Web of Science ®Google Scholar
• G. Tutz and T. Scholz, Ordinal regression modelling between proportional odds and non-proportional odds, Tech. Rep. University of Munich, Institute of Statistics, 2003.
   Google Scholar