PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Approximation of random sums of random variables in insurance

Autorzy
Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The paper deals with approximations of random sums. By random sum we mean a sum of random number of independent and identically distributed random variables. Distribution of this sum is called a compound distribution. The model is especially important in non-life insurance. There are many methods for approximating compound distributions, one of the most popular one is approximation with shifted gamma distribution. In this work we show an alternative way – using kernel density, Fast Fourier Transform and numerical optimization methods – for finding shifted gamma approximations and show results suggesting its superiority over classical method.
Twórcy
autor
  • Department of Applied Mathematics, Lublin University of Technology, Nadbystrzycka 38, 20-618 Lublin, Poland
Bibliografia
  • 1. Billingsley P. Probability and Measure. 3rd ed. John Wiley & Sons, 1995.
  • 2. Bowers N. et al. Actuarial Mathematics. Society of Actuares, Itasca, Illinois, 1986.
  • 3. Cooley J. and Tukey J. An algorithm for the machine calculation of complex Fourier series. Math. Comput. 19, 1965, 297–301.
  • 4. Daykin D., Pentikainen T., and Pesonen E. Practical Risk Theory for Actuaries. Chapman and Hall, 1994.
  • 5. Dutang C., Goulet V., and Pigeon M. Actuar: An R Package for Actuarial Science. Journal of Statistical Software 25(7), 2008, p. 38.
  • 6. Heckman P. and Meyers G.. The calculation of aggregate loss distributions from claim severity and claim count distributions. Proc. Casualty Actuar. Soc. LXX, 1983, 22–61.
  • 7. Nelder J.A. and Mead R. A simplex algorithm for function minimization”. Computer Journal 7, 1965, 308–313.
  • 8. Otto W. Ubezpieczenia majątkowe. WNT, Warszawa 2004.
  • 9. Pitts S. “Nonparametric estimation of compound distributions with application in insurance”. Ann. Inst. Statist. Math. 46(3), 1994, 537–555.
  • 10. R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. Vienna, Austria, 2013. url: http:// www.R-project.org/.
  • 11. Sheather S.J. and Jones M.C. A reliable data-based bandwidth selection method for kernel density estimation. Journal of the Royal Statistical Society, B 53, 1991, 683–690.
  • 12. Willekens E. Asymptotic approximations of compound distributions and some applications. Bull. Soc. Math. Belg. Ser. B 41, 1989, 51–61.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-853a0ae9-f6f5-4f45-98e6-3bf21cd59a5f
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.