Interactions Between Ageing and Risk Properties in the Analysis of Burn-in Problems

Published Online:https://doi.org/10.1287/deca.1120.0236

References

  • Abbas AE, Howard RA. Attribute dominance utility. Decision Anal. (2005) 2(4):185–206LinkGoogle Scholar
  • Arrow KJ. The theory of risk aversion. Aspects of the Theory of Risk-Bearing (1965) (Yrjö Jahnssonin Säätiö, Helsinki) . [Reprinted in Essays in the Theory of Risk-Bearing (1971) (Markham Publishing, Chicago), 90–109.]Google Scholar
  • Barlow RE, Proschan F. Mathematical Theory of Reliability (1965) (John Wiley & Sons, New York) The SIAM Series in Applied MathematicsGoogle Scholar
  • Bebbington M, Lai C-D, Zitikis R. Optimum burn-in time for a bathtub-shaped failure distribution. Methodology Comput. Appl. Probab. (2007) 9(1):1–20CrossrefGoogle Scholar
  • Block HW, Savits TH. Burn-in. Statist. Sci. (1997) 12(1):1–19CrossrefGoogle Scholar
  • Bordley R, LiCalzi M. Decision analysis using targets instead of utility functions. Decision Econom. Finance (2000) 23(1):53–74CrossrefGoogle Scholar
  • Bremaud P. Point Processes and Queues. Martingale Dynamics (1981) (Springer-Verlag, New York) Springer Series in StatisticsCrossrefGoogle Scholar
  • Cha JH. Burn-in procedures for a generalized model. J. Appl. Probab. (2001) 38(2):542–553CrossrefGoogle Scholar
  • Clarotti CA, Spizzichino F. Bayesian burn-in decision procedures. Probab. Engrg. Inform. Sci. (1990) 4(4):437–445CrossrefGoogle Scholar
  • Costantini C, Spizzichino F. Explicit solution of an optimal stopping problem: The burn-in of conditionally exponential components. J. Appl. Probab. (1997) 34(1):267–282CrossrefGoogle Scholar
  • de Finetti B. Sulla preferibilità. Giornale degli Economisti e Annali di Economia (1952) 11:685–709Google Scholar
  • DeGroot MH. Optimal Statistical Decisions (2004) (Wiley, Hoboken, NJ) CrossrefGoogle Scholar
  • Foschi R. Interval bounds for the optimal burn-in times for concave or convex reward functions. (2011) . Working paper, IMT Institute for Advanced Studies, Lucca, ItalyGoogle Scholar
  • Foschi R, Spizzichino F. Semigroups of semicopulas and evolution of dependence at increase of age. Mathware Soft Comput. (2008) 15(1):95–111Google Scholar
  • Glaser RE. Bathtub and related failure rate characterizations. J. Amer. Statist. Assoc. (1980) 75(371):667–672CrossrefGoogle Scholar
  • Gupta RC, Hayakawa Y, Irony T, Xie M. Nonmonotonic failure rates and mean residual life functions. System and Bayesian Reliability (2001) 5(World Scientific, River Edge, NJ) 147–162Series on Quality, Reliability and Engineering StatisticsChap. 9CrossrefGoogle Scholar
  • Herberts T, Jensen U. Optimal stopping in a burn-in model. Commun. Statist. Stochastic Models (1999) 15(5):931–951CrossrefGoogle Scholar
  • Jensen U, Spizzichino F, Mazzuchi TA, Singpurwalla ND, Soyer R. The burn-in problem—A discussion of sequential stop and go strategies. Mathematical Reliability: An Expository Perspective (2004) 67(Kluwer Academic, Boston) 207–229International Series in Operations Research and Management ScienceCrossrefGoogle Scholar
  • Kuo W, Kuo Y. Facing the headaches of early failures: A state-of-the-art review of burn-in decisions. Proc. IEEE (1983) 71(11):1257–1266CrossrefGoogle Scholar
  • Lai C-D, Xie M. Stochastic Ageing and Dependence for Reliability (2004) (Springer, New York) Google Scholar
  • Lynn NJ, Singpurwalla ND. Comment: “Burn-in” makes us feel good. Statist. Sci. (1981) 12(1):13–19Google Scholar
  • Mi J. Burn-in and maintenance policies. Adv. Appl. Probab. (1994) 26(1):207–221CrossrefGoogle Scholar
  • Perlstein D, Jarvis WH, Mazzuchi TA. Bayesian calculation of cost-optimal burn-in test duration for the mixed exponential populations. J. Reliability Engrg. System Safety (2001) 72(3):265–273CrossrefGoogle Scholar
  • Pratt J. Risk aversion in the small and in the large. Econometrica (1964) 32:132–136CrossrefGoogle Scholar
  • Runggaldier WJ, Barlow RE, Clarotti CA, Spizzichino F. On stochastic control concepts for sequential burn-in procedures. Reliability and Decision Making (1993) (Chapman & Hall, London) 211–232CrossrefGoogle Scholar
  • Scarsini M, Shaked M, Shanthikumar JG. Comparing risk and risk aversion. Stochastic Orders and Their Applications, Probability, and Mathematical Statistics (1994) (Academic Press, Boston) 351–378Chap. 12Google Scholar
  • Singpurwalla ND. Reliability and Risk: A Bayesian perspective (2006) (John Wiley & Sons, Chichester, UK) Wiley Series in Probability and StatisticsCrossrefGoogle Scholar
  • Spizzichino F, Barlow RE, Clarotti CA, Spizzichino F. A unifying model for the optimal design of life-testing and burn-in. Reliability and Decision Making (1993) (Chapman & Hall, London) 189–210CrossrefGoogle Scholar
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