Exact Semiparametric Inference and Model Selection for Load-Sharing Systems

Journal article

Fabian Mies, Stefan Bedbur
IEEE Transactions on Reliability, vol. 69, 2019, pp. 863-872

DOI: 10.1109/TR.2019.2935869

arXiv

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APA Click to copy
Mies, F., & Bedbur, S. (2019). Exact Semiparametric Inference and Model Selection for Load-Sharing Systems. IEEE Transactions on Reliability, 69, 863–872. https://doi.org/10.1109/TR.2019.2935869

Chicago/Turabian Click to copy
Mies, Fabian, and Stefan Bedbur. “Exact Semiparametric Inference and Model Selection for Load-Sharing Systems.” IEEE Transactions on Reliability 69 (2019): 863–872.

MLA Click to copy
Mies, Fabian, and Stefan Bedbur. “Exact Semiparametric Inference and Model Selection for Load-Sharing Systems.” IEEE Transactions on Reliability, vol. 69, 2019, pp. 863–72, doi:10.1109/TR.2019.2935869.

BibTeX Click to copy

@article{mies2019a,
  title = {Exact Semiparametric Inference and Model Selection for Load-Sharing Systems},
  year = {2019},
  journal = {IEEE Transactions on Reliability},
  pages = {863-872},
  volume = {69},
  doi = {10.1109/TR.2019.2935869},
  author = {Mies, Fabian and Bedbur, Stefan}
}

As a specific proportional hazard rates model, sequential order statistics can be used to describe the lifetimes of load-sharing systems. Inference for these systems needs to account for small sample sizes, which are prevalent in reliability applications. By exploiting the probabilistic structure of sequential order statistics, in this article, we derive exact finite-sample inference procedures to test for the load-sharing parameters and for the nonparametrically specified baseline distribution, treating the respective other part as a nuisance quantity. This improves upon previous approaches for the model, which either assume a fully parametric specification or rely on asymptotic results. Simulations show that the tests derived are able to detect deviations from the null hypothesis at small sample sizes. Critical values for a prominent case are tabulated.

Keywords: counting process; k-out-of-n system; nonparametric inference; proportional hazard rate; sequential order statistics (SOSs)