New robust tests for the parameters of the Weibull distribution for complete and censored data
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DOI: 10.1007/s00184-013-0454-8
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Cited by:
- Tianyu Liu & Lulu Zhang & Guang Jin & Zhengqiang Pan, 2022. "Reliability Assessment of Heavily Censored Data Based on E-Bayesian Estimation," Mathematics, MDPI, vol. 10(22), pages 1-14, November.
- Jia, Xiang & Wang, Dong & Jiang, Ping & Guo, Bo, 2016. "Inference on the reliability of Weibull distribution with multiply Type-I censored data," Reliability Engineering and System Safety, Elsevier, vol. 150(C), pages 171-181.
- Pierre‐Yves Deléamont & Elvezio Ronchetti, 2022. "Robust inference with censored survival data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(4), pages 1496-1533, December.
- Ingo Hoffmann & Christoph J. Börner, 2021. "The risk function of the goodness-of-fit tests for tail models," Statistical Papers, Springer, vol. 62(4), pages 1853-1869, August.
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Keywords
Weibull distribution; Censored data; Data depth; Simplicial depth; Testing; Robustness against contamination;All these keywords.
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