Abstract
This paper considers the issue of modeling fractional data observed on [0,1), (0,1] or [0,1]. Mixed continuous-discrete distributions are proposed. The beta distribution is used to describe the continuous component of the model since its density can have quite different shapes depending on the values of the two parameters that index the distribution. Properties of the proposed distributions are examined. Also, estimation based on maximum likelihood and conditional moments is discussed. Finally, practical applications that employ real data are presented.
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References
Aitchison J (1955) On the distribution of a positive random variable having a discrete probability mass at the origin. J Am Stat Assoc 50: 901–908
Cook DO, Kieschnick R, McCullough BD (2004) On the heterogeneity of corporate capital structures and its implications. SSRN working paper; available at http://ssrn.com/abstract=671061
Doornik JA, Tibshirani RJ (2006) Object-Oriented matrix language using Ox, 5n ed. Timberlake Consultants Press, London
Efron B, Tibshirani RJ (1993) An introduction to the bootstrap. Chapman and Hall, New York
Feuerverger A (1979) On some methods of analysis for weather experiments. Biometrika 66: 665–668
Ferrari SLP, Cribari–Neto F (2004) Beta regression for modelling rates and proportions. J Appl Stat 31: 799–815
Heller G, Stasinopoulos M, Rigby B (2006) The zero-adjusted inverse Gaussian distribution as a model for insurance claims.In: Hinde J, Einbeck J, Newell J (eds) Proceedings of the 21th International Workshop on Statistical Modelling, 226–233, Ireland, Galway
Hoff A (2007) Second stage DEA: comparison of approaches for modelling the DEA score. Eur J Oper Res 181: 425–435
Ihaka R, Gentleman RR (1996) A language for data analysis and graphics. J Comput Graph Stat 5: 299–314
Johnson N, Kotz S, Balakrishnan N (1995) Continuous univariate distributions. John Wiley and Sons, New York
Kieschnick R, McCullough BD (2003) Regression analysis of variates observed on (0,1): percentages, proportions, and fractions. Stat Model 3: 1–21
Lehmann EL, Casella G (1998) Theory of point estimation. Springer, New York
Lesaffre E, Rizoupoulus D, Tsonaka S (2007) The logistic-transform for bounded outcome scores. Biostatistics 8: 72–85
Mittelhammer RC, Judge GG, Miller DJ (2000) Econometric foundations. Cambridge University Press, New York
Nocedal J, Wright SJ (1999) Numerical optimization. Springer, New York
Ospina R (2006) The zero-inflated beta distribution for fitting a GAMLSS. Contribution to R package gamlss.dist: Extra distributions to be used for GAMLSS modelling. Available at http://r-project.org/CRAN/
Pace L, Salvan A (1997) Principles of statistical inference from a Neo-Fisherian perspective. World Scientific Publishing, Singapore
Stasinopoulos DM, Rigby RA, Akantziliotou C (2006) Instructions on how to use the GAMLSS package in R. Documentation in the current GAMLSS help files http://www.londonmet.ac.uk/gamlss/
Tu W (2002) Zero inflated data. Encycl Environ 4: 2387–2391
Yoo S (2004) A note on an approximation of the mobile communications expenditures distribution function using a mixture model. J Appl Stat 31: 747–775
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Ospina, R., Ferrari, S.L.P. Inflated beta distributions. Stat Papers 51, 111–126 (2010). https://doi.org/10.1007/s00362-008-0125-4
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DOI: https://doi.org/10.1007/s00362-008-0125-4