Hostname: page-component-586b7cd67f-gb8f7 Total loading time: 0 Render date: 2024-11-25T07:11:58.199Z Has data issue: false hasContentIssue false

CONVEX COMPARISONS FOR RANDOM SUMS IN RANDOM ENVIRONMENTS AND APPLICATIONS

Published online by Cambridge University Press:  27 May 2008

José María Fernández-Ponce
Affiliation:
Departamento Estadística e Investigación Operativa Facultad de MatemáticasUniversidad de Sevilla41012 Sevilla, Spain E-mail: [email protected]
Eva María Ortega
Affiliation:
Centro de Investigación Operativa Escuela Politécnica Superior de OrihuelaUniversidad Miguel Hernández03312 Orihuela (Alicante), Spain E-mail: [email protected]
Franco Pellerey
Affiliation:
Dipartimento di Matematica Politecnico di Torino c.so Duca Degli Abruzzi 24 10129 Torino, Italy E-mail: [email protected]

Abstract

Recently, Belzunce, Ortega, Pellerey, and Ruiz [3] have obtained stochastic comparisons in increasing componentwise convex order sense for vectors of random sums when the summands and number of summands depend on a common random environment, which prove how the dependence among the random environmental parameters influences the variability of vectors of random sums. The main results presented here generalize the results in Belzunce et al. [3] by considering vectors of parameters instead of a couple of parameters and the increasing directionally convex order. Results on stochastic directional convexity of families of random sums under appropriate conditions on the families of summands and number of summands are obtained, which lead to the convex comparisons between random sums mentioned earlier. Different applications in actuarial science, reliability, and population growth are also provided to illustrate the main results.

Type
Research Article
Copyright
Copyright © Cambridge University Press 2008

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1Bäuerle, N. & Müller, A. (1998). Modeling and comparing dependencies in multivariate risk portfolios. Astin Bulletin 28: 5976.CrossRefGoogle Scholar
2Bäuerle, N. & Rolski, T. (1998). A monotonicity result for the work-load in Markov-modulates queues. Journal of Applied Probability 35: 741747.CrossRefGoogle Scholar
3Belzunce, F., Ortega, E.M., Pellerey, F. & Ruiz, J.M. (2006). Variability of total claim amounts under dependence between claims severity and number of events. Insurance: Mathematics and Economics 38: 460468.Google Scholar
4Chang, C.-S., Chao, X.L., Pinedo, M. & Shanthikumar, J.G. (1991). Stochastic convexity for multidimensional processes and applications. IEEE Transactions on Automated Control 36: 13471355.CrossRefGoogle Scholar
5Chang, C.-S., Shanthikumar, J.G. & Yao, D.D. (1994). Stochastic convexity and stochastic majorization. In Yao, D.D., (ed.) Stochastic modeling and analysis of manufacturing systems New York: Springer-Verlag.Google Scholar
6Denuit, M., Dhaene, J., Goovaerts, M. & Kaas, R. (2005). Actuarial theory for dependent risks. Measures, orders and models Chichester, UK: Wiley.CrossRefGoogle Scholar
7Denuit, M., Genest, C. & Marceau, E. (2002). Criteria for the stochastic ordering of random sums, with acturial applications. Scandinavian Actuarial Journal 1: 316.CrossRefGoogle Scholar
8Denuit, M. & Müller, A. (2002). Smooth generators of integral stochastic orders. Annals of Applied Probability 12: 11741184.CrossRefGoogle Scholar
9Esary, J.D., Marshall, A.W. & Proschan, F. (1973). Shock models and wear processes. The Annals of Probability 1: 627649.CrossRefGoogle Scholar
10Frostig, E. (2001). Comparison of portfolios which depend on multivariate Bernoulli random variables with fixed marginals. Insurance: Mathematics and Economics 29: 319331.Google Scholar
11Frostig, E. (2003). Ordering ruin probabilities for dependent claim streams. Insurance: Mathematics and Economics 32: 93114.Google Scholar
12Frostig, E. & Denuit, M. (2006). Monotonicity results for portfolios with heterogeneous claims arrivals processes. Insurance: Mathematics and Economics 38: 484494.Google Scholar
13Hu, T. & Ruan, L. (2004). A note on multivariate stochastic comparinsons of Bernoulli random variables. Journal of Statistical Planning and Inference 126: 281288.CrossRefGoogle Scholar
14Hu, T. & Wu, Z. (1999). On dependence of risks and stop-loss premiums. Insurance: Mathematics and Economics 24: 323332.Google Scholar
15Joe, H. (1997). Multivariate models and dependence concepts London: Chapman & Hall.Google Scholar
16Kimmel, M. & Axelrod, D.E. (2002). Branching processes in biology New York: Springer-Verlag.CrossRefGoogle Scholar
17Kulik, R. (2003). Stochastic comparison of multivariate random sums. Applicationes Mathematicae 30: 379387.CrossRefGoogle Scholar
18Lefèvre, C. & Utev, S. (1996). Comparing sums of exchangeable Bernoulli random variables. Journal of Applied Probability 33: 285310.CrossRefGoogle Scholar
19Li, H. & Xu, S. (2001). Directionally convex comparison of correlated first passage times. Methodology and Computing in Applied Probability 3: 365378.CrossRefGoogle Scholar
20Lillo, R.E., Pellerey, F., Semeraro, P. & Shaked, M. (2003). On the preservation of the supermodular order under multivariate claim models. Ricerche di Matematica 52: 7381.Google Scholar
21Lillo, R.E. & Semeraro, P., (2004). Stochastic bounds for discrete-time claim processes with correlated risks. Scandinavian Actuarial Journal 1: 113.CrossRefGoogle Scholar
22Marshall, A.W. & Olkin, I. (1979). Inequalities: Theory of majorization and its Applications New York: Academic Press.Google Scholar
23Meester, L.E. & Shanthikumar, J.G. (1993). Regularity of stochastic processes. A theory based on directional convexity. Probability in the Engineering and Informational Sciences 7: 343360.CrossRefGoogle Scholar
24Meester, L.E. & Shanthikumar, J.G. (1999). Stochastic convexity on general space. Mathematics of Operations Research 24: 472494.CrossRefGoogle Scholar
25Müller, A. (1997). Stop-loss order for portfolios of dependent risks. Insurance: Mathematics and Economics 21: 219223.Google Scholar
26Müller, A. & Stoyan, D. (2002). Comparison methods for stochastic models and risks, Chichester, UK: Wiley.Google Scholar
27Pellerey, F. (1993). Partial orderings under cumulative damage shock models. Advances in Applied Probability 25: 939946.CrossRefGoogle Scholar
28Pellerey, F. (1997). Some new conditions for the increasing convex comparison of risks. Scandinavian Actuarial Journal 97: 3847.CrossRefGoogle Scholar
29Pellerey, F. (1999). Stochastic comparisons for multivariate shock models. Journal of Multivariate Analysis 71: 4255.CrossRefGoogle Scholar
30Pellerey, F. (2006). Comparison results for branching processes in random environments. Rapporto interno 9, Dipartimento di Matematica, Politecnico di Torino, Torino.Google Scholar
31Rockafellar, R.T. (1970). Convex analysis Princeton University Press, Princeton, NJ.CrossRefGoogle Scholar
32Rolski, T., Schmidli, H., Schmidt, V. & Teugels, J. (1999). Stochastic processes for insurance and finance New York: Wiley.CrossRefGoogle Scholar
33Ross, S.M. (1992). Applied probability models with optimization applications New York: Dover.Google Scholar
34Ross, S.M. & Schechner, Z. (1983). Some reliability applications of the variability ordering. Operations Research 32: 679687.CrossRefGoogle Scholar
35Rüschendorf, L. (2004). Comparison of multivariate risks and positive dependence. Advances in Applied Probability 41: 391406.CrossRefGoogle Scholar
36Shaked, M. & Shanthikumar, J.G. (1988). Stochastic convexity and its applications. Advances in Applied Probability 20: 427446.CrossRefGoogle Scholar
37Shaked, M. & Shanthikumar, J.G. (1988). Temporal stochastic convexity and concavity. Stochastic Processes and Their Applications 27: 120.CrossRefGoogle Scholar
38Shaked, M. & Shanthikumar, J.G. (1990). Parametric stochastic convexity and concavity of stochastic processes. Annals of the Institute of Statistical Mathematics 42: 509531.CrossRefGoogle Scholar
39Shaked, M. & Shanthikumar, J.G. (2007). Stochastic orders New-York: Springer.CrossRefGoogle Scholar
40Shaked, M. & Shanthikumar, J.G. (1997). Supermodular stochastic orders and positive dependence of random vectors. Journal of Multivariate Analysis 61: 86101.CrossRefGoogle Scholar