Hostname: page-component-586b7cd67f-gb8f7 Total loading time: 0 Render date: 2024-11-25T23:57:30.752Z Has data issue: false hasContentIssue false

An age-dependent counting process generated from a renewal process

Published online by Cambridge University Press:  01 July 2016

Ushio Sumita*
Affiliation:
University of Rochester
J. George Shanthikumar*
Affiliation:
University of California, Berkeley
*
Postal address: W. E. Simon Graduate School of Business Administration, University of Rochester, Rochester, NY 14627, USA.
∗∗Postal address: School of Business Administration, University of California, Berkeley, CA 94720, USA.

Abstract

Let {N(t)} be a renewal process having the associated age process {X(t)}. Of interest is the counting process {M(t)} characterized by a non-homogeneous Poisson process with age-dependent intensity function λ (X(t)). The trivariate process {Y(t) = [M(t), N(t), X(t)]} is analyzed obtaining its Laplace transform generating function explicitly. Based on this result, asymptotic behavior of {S(t) = cM(t) + dN(t)} as t → ∞ is discussed. Furthermore, a sufficient condition is given under which {M(t), –N(t), X(t)} is stochastically monotone and associated. This condition also assures increasing stochastic convexity of {M(t)}. The usefulness of these results is demonstrated through an application to the age-dependent minimal repair problem.

Type
Research Article
Copyright
Copyright © Applied Probability Trust 1988 

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.)

Footnotes

Research partially supported by NSF Grant ECS-8600992 and by the IBM Program of Support for Education in the Management of Information Systems.

Research partially supported by NSF Grant ECS-8601210.

References

[1] Anscombe, F. J. (1952) Large-sample theory of sequential estimation. Proc. Camb. Phil. Soc. 48, 600607.CrossRefGoogle Scholar
[2] Block, H. W., Borges, W. S. and Savits, T. H. (1985) Age-dependent minimal repair. J. Appl. Prob. 22, 370385.CrossRefGoogle Scholar
[3] Brown, M. (1981) Further monotonicity properties for specialized renewal processes. Ann. Prob. 9, 891895.CrossRefGoogle Scholar
[4] Harris, T. E. (1977) A correlation inequality for Markov processes in partially ordered state spaces. Ann. Prob. 5, 451454.CrossRefGoogle Scholar
[5] Jacobs, P. A. (1986) First passage times for combinations of random loads. SIAM J. Appl. Math. 46, 643656.Google Scholar
[6] Ross, S. M. (1983) Stochastic Processes. Wiley, New York.Google Scholar
[7] Shaked, M. and Shanthikumar, J. G. (1987) Temporal stochastic convexity and concavity. Stoch. Proc. Appl. 27, 120.CrossRefGoogle Scholar
[8] Shaked, M. and Shanthikumar, J. G. (1988) Stochastic convexity and its applications. Adv. Appl. Prob. 20, 427446.CrossRefGoogle Scholar
[9] Shanthikumar, J. G. and Sumita, U. (1983) General shock models associated with correlated renewal sequences. J. Appl. Prob. 20, 600614.CrossRefGoogle Scholar
[10] Shanthikumar, J. G. and Sumita, U. (1984) Distribution properties of the system failure time in a general shock model. Adv. Appl. Prob. 16, 363377.CrossRefGoogle Scholar
[11] Stoyan, D. (1983) Comparison Methods for Queues and Other Stochastic Models. Wiley, New York.Google Scholar
[12] Sumita, U. and Shanthikumar, J. G. (1985) A class of correlated cumulative shock models. Adv. Appl. Prob. 17, 347366.CrossRefGoogle Scholar