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Hazard Rate Properties of a General Counting Process Stopped at an Independent Random Time

Published online by Cambridge University Press:  14 July 2016

F. G. Badia*
Affiliation:
University of Zaragoza
*
Postal address: Department of Statistical Methods, University of Zaragoza, Maria de Luna 3, Zaragoza, 50018, Spain. Email address: [email protected]
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Abstract

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In this work we provide sufficient conditions under which a general counting process stopped at a random time independent from the process belongs to the reliability decreasing reversed hazard rate (DRHR) or increasing failure rate (IFR) class. We also give some applications of these results in generalized renewal and trend renewal processes stopped at a random time.

Type
Research Article
Copyright
Copyright © Applied Probability Trust 2011 

References

[1] Badía, F. G. and Sangüesa, C. (2008). Preservation of reliability classes under mixtures of renewal processes. Prob. Eng. Inf. Sci. 22, 117.CrossRefGoogle Scholar
[2] Barlow, R. E. and Proschan, F. (1965). Mathematical Theory of Reliability. John Wiley, New York.Google Scholar
[3] Barlow, R. E. and Proschan, F. (1975). Statistical Theory of Reliability and Life Testing. Holt, Reinhart and Winston, New York.Google Scholar
[4] Block, H. W. and Savits, T. H. (1980). Laplace transforms for classes of life distributions. Ann. Prob. 8, 465474.Google Scholar
[5] Block, H. W., Savits, T. H. and Sing, H. (1998). The reversed hazard rate function. Prob. Eng. Inf. Sci. 12, 6990.Google Scholar
[6] Esary, J. D., Marshall, A. W. and Proschan, F. (1973). Shock models and wear processes. Ann. Prob. 1, 627649.Google Scholar
[7] Grandell, J. (1997). Mixed Poisson Processes. Chapman and Hall, London.Google Scholar
[8] Kijima, M. and Sumita, U. (1986). A useful generalization of renewal theory: counting processes governed by nonnegative Markovian increments. J. Appl. Prob. 23, 7188.Google Scholar
[9] Lai, C.-D. and Xie, M. (2006). Stochastic Ageing and Dependence for Reliability. Springer, New York.Google Scholar
[10] Lindqvist, B. H., Elvebakk, G. and Heggland, K. (2003). The trend-renewal process for statistical analysis of repairable systems. Technometrics 45, 3144.Google Scholar
[11] Marshall, A. W. and Olkin, I. (2007). Life Distributions. Springer, New York.Google Scholar
[12] Müller, A. and Stoyan, D. (2002). Comparison Methods for Stochastic Models and Risks. John Wiley, Chichester.Google Scholar
[13] Ross, S. M., Shanthikumar, J. G. and Zhu, Z. (2005). On increasing-failure-rate random variables. J. Appl. Prob. 42, 797809.Google Scholar
[14] Sengupta, D. and Nanda, A. K. (1999). Log-concave and concave distributions in reliability. Naval Res. Logistics 46, 419433.Google Scholar
[15] Shaked, M. and Shanthikumar, J. G. (2007). Stochastic Orders. Springer, New York.Google Scholar
[16] Shanthikumar, J. G. and Yao, D. D. (1991). Bivariate characterization of some stochastic order relations. Adv. Appl. Prob. 23, 642659.CrossRefGoogle Scholar