On Modelling Insurance Data by Using a Generalized Lognormal Distribution

Authors

  • Victoriano J. García Departamento de Estadística e Investigación Operativa Universidad de Cádiz (España)
  • Emilio Gómez-Deníz Departamento de Métodos Cuantitativos e Instituto TiDES Universidad de Las Palmas de Gran Canaria (España)
  • Francisco J. Vázquez-Polo Departamento de Métodos Cuantitativos e Instituto TiDES Universidad de Las Palmas de Gran Canaria (España)

DOI:

https://doi.org/10.46661/revmetodoscuanteconempresa.2209

Keywords:

Heavy-tailed, insurance, lognormal distribution, loss distribution, seguros, distribución lognormal, función de perdidas, colas pesadas

Abstract

In this paper, a new heavy-tailed distribution is used to model data with a strong right tail, as often occurs in practical situations. The distribution proposed is derived from the lognormal distribution, by using the Marshall and Olkin procedure. Some basic properties of this new distribution are obtained and we present situations where this new distribution correctly reflects the sample behaviour for the right tail probability. An application of the model to dental insurance data is presented and analysed in depth. We conclude that the generalized lognormal distribution proposed is a distribution that should be taken into account among other possible distributions for insurance data in which the properties of a heavy-tailed distribution are present.

 

Downloads

Download data is not yet available.

References

Beirlant, J., Matthys, G., and Dierckx, G. (2001). Heavy-tailed distributions and rating. Astin Bulletin, 31, 1, 37-58.

Blishke, W. and Murthy, D. (2000). Reliability: Modeling, Prediction, and Optimization. Wiley.

Chen, G. (1995). Generalized log-normal distributions with reliability application. Computational Statistics and Data Analysis, 19, 309-319.

Dutta, K. and Perry, J. (2006). A tale of tails: an empirical analysis of loss distribution models for estimating operational risk capital. Federal Reserve Bank of Boston, Working Paper , 06-13, 2006 Series.

García, V., Gómez-Déniz, E., and Vázquez-Polo, F.J. (2010). A new skew generalization of the Normal distribution: properties and applications. Computational Statistics and Data Analysis, 54, 2021-2034.

Ghitany, M.E. (2005). Marshall-Olkin extended Pareto distribution and its applications. International Journal of Applied Mathematics, 18, 17-32.

Ghitany, M.E., Al-Awadhi, F.A., and Alkhalfan, L.A. (2007). Marshall-Olkin extended Weibull distribution and its applications to censored data. Communications to Statistics: Theory and Methods, 36, 1855- 1866.

Ghitany, M.E., Al-Hussaini, E.K., and Al-Jarallah, R.A. (2005). Marshall-Olkin extended Lomax distribution and its applications to censored data. Journal of Applied Statistics, 32, 1025-1034.

Gómez-Déniz, E. (2010). Another generalization of the geometric distribution. Test, 19, 399-415.

Gupta, R.C., Gupta, P.L., and Gupta, R.D. (1998). Modeling failure time data by Lehmann alternatives. Communications in Statistics: Theory and Methods, 27, 887-904.

Gupta, R.D. and Kundu, D. (1999). Generalized Exponential Distributions. Australian and New Zealand Journal of Statistics, 41, 2, 173-188.

Hogg, R.V. and Klugman, S.A. (1984). Loss Distributions. Wiley Series in Probability and Mathematical Statistics.

Jose, K.K., Naik, S.R., and Ristić, M.M. (2010). Marshall-Olkin q-Weibul distribution and max-min processes. Statistical Papers, 51, 837-851.

Klugman, S.A. (1986). Loss distributions. In Actuarial Mathematics. Proceedings of Symposia in Applied Mathematics. American Mathematical Society, pp. 31-55.

Klugman, S.A., Panjer, H.H., and Willmot, G.E. (2008). Loss models: from data to decisions, Wiley.

Lehmann, E.L. (1959). The power of rank test. Annals of Mathematical Statistics, 24, 23-43.

Marshall, A.W. and Olkin, I. (1997). A new method for adding a parameter to a family of distributions with application to the exponential and Weibull families. Biometrika, 84, 3, 641-652.

Martín, J. and Pérez, C.J. (2009). Bayesian analysis of a generalized lognormal distribution. Computational Statistics and Data Analysis, 53, 1377-1387.

Prendergast, J., O'Driscoll, E., and Mullen, E. (2005). Investigation into the correct statistical distribution for oxide breakdown over oxide thickness range. Microelectronics Reliability, 45, 5-6, 973-977.

Rolski, T., Schmidli, H. Schmidt, V., and Teugel, J. (1999). Stochastic processes for insurance and finance. John Wiley & Sons.

Sarabia, J.M. and Castillo, E. (2005). About a class of max-stable families with applications to income distributions. Metron, LXIII, 3, 505-527.

Sobkowicz, P; Thelwall, M.; Buckley, K.; Paltoglou, G., and Sobkowicz, A. (2013). Lognormal distributions of user post lengths in Internet discussions - a consequence of the Weber-Fechner law? EPJ Data Science 2013, 2:2. Available at http://www.epjdatascience.com/content/2/1/2.

Published

2016-11-04

How to Cite

García, V. J., Gómez-Deníz, E., & Vázquez-Polo, F. J. (2016). On Modelling Insurance Data by Using a Generalized Lognormal Distribution . Journal of Quantitative Methods for Economics and Business Administration, 18, Páginas 146 a 162. https://doi.org/10.46661/revmetodoscuanteconempresa.2209

Issue

Section

Articles