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Optimal sample size allocation for multi-level stress testing with exponential regression under type-I censoring

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  • Ping Shing Chan
  • Narayanaswamy Balakrishnan
  • Hon Yiu So
  • Hon Keung Tony Ng

Abstract

In this article, we discuss the optimal allocation problem in a multi-level accelerated life-testing experiment under Type-I censoring when an exponential regression model is used for the analysis. We derive the expected Fisher information matrix and use it to obtain the asymptotic variance–covariance matrix of the maximum likelihood estimators (MLEs). We then consider the optimal allocation under the D-optimality criterion, and present an algorithm for determining the optimal allocation. A numerical example is presented for the purpose of illustration. The optimal allocation depends on the model parameters and so a sensitivity analysis of the optimal allocation to misspecification of the model parameters is carried out as well.

Suggested Citation

  • Ping Shing Chan & Narayanaswamy Balakrishnan & Hon Yiu So & Hon Keung Tony Ng, 2016. "Optimal sample size allocation for multi-level stress testing with exponential regression under type-I censoring," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 45(6), pages 1831-1852, March.
    • Handle: RePEc:taf:lstaxx:v:45:y:2016:i:6:p:1831-1852
    DOI: 10.1080/03610926.2015.1078474
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    Cited by:

    1. Mohamed Sief & Xinsheng Liu & Abd El-Raheem Mohamed Abd El-Raheem, 2024. "Inference for a constant-stress model under progressive type-II censored data from the truncated normal distribution," Computational Statistics, Springer, vol. 39(5), pages 2791-2820, July.

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