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Optimal allocation of change points in simple step-stress experiments under Type-II censoring

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  • Kateri, Maria
  • Kamps, Udo
  • Balakrishnan, Narayanaswamy

Abstract

In simple step-stress experiments under Type-II censoring with the cumulative exposure model and exponentially distributed lifetimes, maximum likelihood estimates (MLE) of the expected lifetimes may not exist due to the absence of failure times either before or after the stress change point. For this reason, when planning a step-stress experiment, the change point could be chosen so as to minimize the probability of non-existence of the MLE. These non-existence probabilities are examined and compared in the one- as well as the two-sample situations. Moreover, the optimal allocations of the change points are discussed and the effects of the use of non-optimal choices for the change points are assessed.

Suggested Citation

  • Kateri, Maria & Kamps, Udo & Balakrishnan, Narayanaswamy, 2011. "Optimal allocation of change points in simple step-stress experiments under Type-II censoring," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 236-247, January.
    • Handle: RePEc:eee:csdana:v:55:y:2011:i:1:p:236-247
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    References listed on IDEAS

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    1. Abdel-Hamid, Alaa H. & AL-Hussaini, Essam K., 2009. "Estimation in step-stress accelerated life tests for the exponentiated exponential distribution with type-I censoring," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 1328-1338, February.
    2. Wang, Bing Xing, 2009. "Testing for the validity of the assumptions in the exponential step-stress accelerated life-testing model," Computational Statistics & Data Analysis, Elsevier, vol. 53(7), pages 2702-2709, May.
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    Cited by:

    1. Benjamin Laumen & Erhard Cramer, 2019. "Stage life testing," Naval Research Logistics (NRL), John Wiley & Sons, vol. 66(8), pages 632-647, December.
    2. Fernández, Arturo J. & Pérez-González, Carlos J., 2012. "Optimal acceptance sampling plans for log-location–scale lifetime models using average risks," Computational Statistics & Data Analysis, Elsevier, vol. 56(3), pages 719-731.

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