Hostname: page-component-586b7cd67f-dlnhk Total loading time: 0 Render date: 2024-11-26T04:55:43.629Z Has data issue: false hasContentIssue false

An extended class of univariate and multivariate generalized Pólya processes

Published online by Cambridge University Press:  11 July 2022

Ji Hwan Cha*
Affiliation:
Ewha Womans University
*
*Postal address: Department of Statistics, Ewha Womans University, Seoul, 120-750, Korea. Email: [email protected]

Abstract

In this paper, we consider an extended class of univariate and multivariate generalized Pólya processes and study its properties. In the generalized Pólya process considered in [8], each occurrence of an event increases the stochastic intensity of the counting process. In the extended class studied in this paper, on the contrary, it decreases the stochastic intensity of the process, which induces a kind of negative dependence in the increments in the disjoint time intervals. First, we define the extended class of generalized Pólya processes and derive some preliminary results which will be used in the remaining part of the paper. It is seen that the extended class of generalized Pólya processes can be viewed as generalized pure death processes, where the death rate depends on both the state and the time. Based on the preliminary results, the main properties of the multivariate extended generalized Pólya process and meaningful characterizations are obtained. Finally, possible applications to reliability modeling are briefly discussed.

Type
Original Article
Copyright
© The Author(s), 2022. Published by Cambridge University Press on behalf of Applied Probability Trust

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

Arriaza, A., Navarro, J. and Suárez-Llorens, A. (2018). Stochastic comparisons of replacement policies in coherent systems under minimal repair. Naval Res. Logistics 65, 550565.CrossRefGoogle Scholar
Aven, T. (1996). Condition based replacement policies—a counting process approach. Reliab. Eng. System Safety 51, 275281.CrossRefGoogle Scholar
Aven, T. and Jensen, U. (1999). Stochastic Models in Reliability. Springer, New York.CrossRefGoogle Scholar
Aven, T. and Jensen, U. (2000). A general minimal repair model. J. Appl. Prob. 37, 187197.CrossRefGoogle Scholar
Badía, F. G., Berrade, M. D. and Campos, C. A. (2001). Optimization of inspection intervals based on cost. J. Appl. Prob. 38, 872881.CrossRefGoogle Scholar
Badía, F. G., Berrade, M. D. and Campos, C. A. (2002). Maintenance policy for multivariate standby/operating units. Appl. Stoch. Models Business Industry 18, 147155.CrossRefGoogle Scholar
Barbu, V. S. and Limnios, N. (2008). Semi-Markov Chains and Hidden Semi-Markov Models toward Applications: Their Use in Reliability and DNA Analysis. Springer, New York.Google Scholar
Cha, J. H. (2014). Characterization of the generalized Pólya process and its applications. Adv. Appl. Prob. 46, 11481171.CrossRefGoogle Scholar
Cha, J. H. and Giorgio, M. (2016). On a class of multivariate counting processes. Adv. Appl. Prob. 48, 443462.CrossRefGoogle Scholar
Cox, D. R. and Lewis, P. A. W. (1972). Multivariate point processes. In Proceedings of the Sixth Berkeley Symposium on Mathematical Statistics and Probability, Vol. III, University of California Press, Berkeley, pp. 401448.CrossRefGoogle Scholar
Daley, D. J. and Vere-Jones, D. (1988). An Introduction to the Theory of Point Processes. Springer, New York.Google Scholar
Finkelstein, M. (2004). Minimal repair in heterogeneous populations. J. Appl. Prob. 41, 281286.CrossRefGoogle Scholar
Finkelstein, M. (2008). Failure Rate Modelling for Reliability and Risk. Springer, London.Google Scholar
Johnson, N. L., Kots, S. and Balakrishnan, N. (1997). Discrete Multivariate Distributions. John Wiley, New York.Google Scholar
Kao, P. C. (1997). An Introduction to Stochastic Processes. Duxbury Press, Belmont.Google Scholar
Last, G. and Brandt, A. (1995). Marked Point Processes on the Real Line: the Dynamical Approach. Springer, New York.Google Scholar
Lee, H. and Cha, J. H. (2016). New stochastic models for preventive maintenance and maintenance optimization. Europ. J. Operat. Res. 255, 8090.CrossRefGoogle Scholar
Lefebvre, M. and Guilbault, J. L. (2008). Using filtered Poisson processes to model a river flow. Appl. Math. Modelling 32, 27922805.CrossRefGoogle Scholar
Limnios, N. and Oprisan, G. (2001). Semi-Markov Processes and Reliability. Birkhäuser, Boston.CrossRefGoogle Scholar
Mi, J. (1994). Burn-in and maintenance policies. Adv. Appl. Prob. 26, 207221.CrossRefGoogle Scholar
Navarro, J., Arriaza, A. and Suárez-Llorens, A. (2019). Minimal repair of failed components in coherent systems. Europ. J. Operat. Res. 279, 951964.CrossRefGoogle Scholar
Pickands, J., III (1971). The two-dimensional Poisson process and extremal processes. J. Appl. Prob. 8, 745756.CrossRefGoogle Scholar
Ross, S. M. (1996). Stochastic Processes. John Wiley, New York.Google Scholar
Sherif, Y. S. and Smith, M. L. (1981). Optimal maintenance models for systems subject to failure—a review. Naval Res. Logistics 28, 4774.CrossRefGoogle Scholar
Taylor, H. M. and Karlin, S. (1998). An Introduction to Stochastic Modeling. Academic Press, London.Google Scholar
Valdez-Flores, C. and Feldman, R. M. (1989). A survey of preventive maintenance models for stochastically deteriorating single-unit systems. Naval Res. Logistics 36, 419446.3.0.CO;2-5>CrossRefGoogle Scholar
Wang, H. (2002). A survey of maintenance policies of deteriorating systems. Europ. J. Operat. Res. 139, 469489.CrossRefGoogle Scholar