Power divergence approach for one-shot device testing under competing risks
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Publication date
2022
Authors
Balakrishnan, Narayanaswamy
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Elsevier
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[2] Balakrishnan, N., So. H., and Ling, M. H. (2016b). EM algorithm for one-shot device testing with competing risks under Weibull distribution. IEEE Transactions on Reliability, 65(2), 973-991.
[3] Balakrishnan, N., Castilla, E., Martin N. and Pardo, L. (2019a). Robust estimators and test-statistics for one-shot device testing under the exponential distribution. IEEE Transactions on Information Theory, 65(5), 3080-3096.
[4] Balakrishnan, N., Castilla, E., Martin N. and Pardo, L. (2019b). Robust estimators for one-shot device testing data under gamma lifetime model with an application to a tumor toxicological data. Metrika, 82(8), 991{1019.
[5] Balakrishnan, N., Castilla, E., Martin N. and Pardo, L. (2019c). Robust inference for one-shot device testing data under Weibull lifetime model. IEEE Transactions on Reliability, DOI: 10.1109/TR.2019.2954385.
[6] Balakrishnan, N., Castilla, E., Martin N. and Pardo, L. (2020). Robust inference for one-shot device testing data under exponential lifetime model with multiple stresses Under revision
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Abstract
Most work on one-shot devices assume that there is only one possible cause of device failure. However, in practice, it is often the case that the products under study can experience any one of various possible causes of failure. Robust estimators and Wald-type tests are developed here for the case of one-shot devices under competing risks. An extensive simulation study illustrates the robustness of these divergence-based estimators and test procedures based on them. A data-driven procedure is proposed for choosing the optimal estimator for any given data set which is then applied to an example in the context of survival analysis.