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A computational framework for censoring design in monitoring lifetime data from one-dimensional exponential families

Zhanzhongyu Gao et al.

Journal of Quality Technology2026https://doi.org/10.1080/00224065.2025.2612367article
ABDC A
Weight
0.50

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https://doi.org/https://doi.org/10.1080/00224065.2025.2612367

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@article{zhanzhongyu2026,
  title        = {{A computational framework for censoring design in monitoring lifetime data from one-dimensional exponential families}},
  author       = {Zhanzhongyu Gao et al.},
  journal      = {Journal of Quality Technology},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1080/00224065.2025.2612367},
}

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A computational framework for censoring design in monitoring lifetime data from one-dimensional exponential families

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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