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Burn-in and covariates

Part of:Stochastic processesSufficiency and information

Published online by Cambridge University Press:  14 July 2016

Nader Ebrahimi
Nader Ebrahimi*
Affiliation:
Northern Illinois University
*
∗ Postal address: Division of Statistics, Northern Illinois University, DeKalb, IL 60115, USA. Email address: nader@math.niu.edu
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Abstract

Burn-in is a widely used engineering procedure useful for eliminating ‘weak’ items and consequently improving the quality of remaining items. The quality of items can be measured via various performance characteristics. In the present paper we develop new performance criteria for the burn-in method. Our criteria not only take into account the reliability of an item, they also incorporate covariates.

Keywords

Burn-incompound Poisson processhomogeneous Poisson processgeometric Brownian motionstandard Brownian motion

MSC classification

Primary:62B10: Information-theoretic topics
Secondary:60G15: Gaussian processes

Information

Type
Research Papers
Information
Journal of Applied Probability , Volume 41 , Issue 3 , September 2004, pp. 735 - 745
Copyright
Copyright © Applied Probability Trust 2004 

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References

Block, H. W., and Savits, T. H. (1997). Burn-in. Statist. Sci. 12, 1–19.Google Scholar
Cha, J. H. (2000). On a better burn-in procedure. J. Appl. Prob. 37, 1099–1103.10.1239/jap/1014843087Google Scholar
Cha, J. H. (2001). Burn in procedures for a generalized model. J. Appl. Prob. 38, 542–553.10.1239/jap/996986761Google Scholar
Clarotti, C. A., and Spizzichino, F. (1990). Bayes burn-in decision procedures. Prob. Eng. Inf. Sci. 4, 437–445.10.1017/S0269964800001741Google Scholar
Jensen, F., and Peterson, N. E. (1982). Burn-In. John Wiley, New York.Google Scholar
Karlin, S., and Taylor, H. (1975). A First Course in Stochastic Processes. Academic Press, New York.Google Scholar
Karlin, S., and Taylor, H. (1981). A Second Course in Stochastic Processes. Academic Press, New York.Google Scholar
Kim, K., and Kuo, W. (2003). A general model of heterogeneous system lifetimes and conditions for system burn-in. Naval Res. Logistics 50, 364–380.10.1002/nav.10067Google Scholar
Lai, C. D., Xie, M., and Murthy, D. N. P. (2001). Bathtub-shaped failure rate life distributions. In Handbook of Statistics, Vol. 20, Advances in Reliability, eds Balakrishnan, N. and Rao, C. R., North-Holland, Amsterdam, pp. 69–105.Google Scholar
Mi, J. (1994). Maximization of survival probability and its application. J. Appl. Prob. 31, 1026–1033.10.2307/3215326Google Scholar
Siegmund, S. (1986). Boundary crossing probabilities and statistical applications. Ann. Statist. 14, 361–404.10.1214/aos/1176349928Google Scholar
Tseng, S. T., Tang, J., and Ku, I. H. (2003). Determination of burn-in parameters and residual life of highly reliable products. Naval Res. Logistics 50, 1–14.10.1002/nav.10042Google Scholar
Watson, G. S., and Wells, W. T. (1961). On the possibility of improving mean useful life of items by eliminating those with short lives. Technometrics 3, 281–298.10.1080/00401706.1961.10489946Google Scholar