Hostname: page-component-cd9895bd7-hc48f Total loading time: 0 Render date: 2024-12-23T18:37:51.759Z Has data issue: false hasContentIssue false

A Note on the Failure Rates in Finite Mixed Populations

Published online by Cambridge University Press:  04 February 2016

Ji Hwan Cha*
Affiliation:
Ewha Womans University
Massimiliano Giorgio*
Affiliation:
Second University of Naples
*
Postal address: Department of Statistics, Ewha Womans University, Seoul, 120-750, Korea. Email address: [email protected]
∗∗ Postal address: Department of Aerospace and Mechanical Engineering, Second University of Naples, 81031 Aversa (CE), Italy. Email address: [email protected]
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

Almost all populations existing in the real world are finite populations. Specifically, in the areas relevant to lifetime modeling and analysis, finite populations are frequently encountered. However, descriptions of failure/survival patterns of elements in the finite population have not yet been properly established. In particular, it is questionable whether the ordinary failure rate can be defined for finite populations in the same way and whether the corresponding interpretations are still valid. In this paper we consider two kinds of finite mixed population and provide new definitions for their failure rates. Then we clarify the notion of failure rate in finite populations.

Type
Research Article
Copyright
© Applied Probability Trust 

References

Aven, T. and Jensen, U. (1999). Stochastic Models in Reliability. Springer, New York.CrossRefGoogle Scholar
Block, H. W., Savits, T. H. and Wondmagegnehu, E. T. (2003). Mixtures of distributions with increasing linear failure rates. J. Appl. Prob. 40, 485504.CrossRefGoogle Scholar
Cha, J. H. (2000). On a better burn-in procedure. J. Appl. Prob. 37, 10991103.Google Scholar
Cha, J. H. (2001). Burn-in procedures for a generalized model. J. Appl. Prob. 38, 542553.Google Scholar
Cha, J. H. (2003). A further extension of the generalized burn-in model. J. Appl. Prob. 40, 264270.Google Scholar
Ebrahimi, N. (1996). Engineering notion of mean-residual-life {&} hazard-rate for finite populations with known distributions. IEEE Trans. Reliab. 45, 362368.Google Scholar
Ebrahimi, N. (2004). Burn-in and covariates. J. Appl. Prob. 41, 735745.Google Scholar
Finkelstein, M. (2008). Failure Rate Modelling for Reliability and Risk. Springer, London.Google Scholar
Finkelstein, M. (2009). Understanding the shape of the mixture failure rate (with engineering and demographic applications). Appl. Stoch. Models Business Industry 25, 643663.Google Scholar
Finkelstein, M. S. and Esaulova, V. (2001). Modelling a failure rate for the mixture of distribution functions. Prob. Eng. Inf. Sci. 15, 383400.Google Scholar
Jiang, R. and Murthy, D. N. P. (1995). Modelling failure data by mixture of two Weibull distributions: a graphical approach. IEEE Trans. Reliab. 44, 477488.Google Scholar
Mi, J. (1994). Burn-in and maintenance policies. Adv. Appl. Prob. 26, 207221.Google Scholar
Navarro, J. and Hernandez, P. J. (2004). How to obtain bathtub-shaped failure rate models from normal mixtures. Prob. Eng. Inf. Sci. 18, 511531.Google Scholar
Vaupel, J. W. and Zhang, Z. (2010). Attrition in heterogeneous cohorts. Demographic Res. 23, 737748.Google Scholar