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Code and Data Repository for Efficient input uncertainty quantification for ratio estimator

Linyun He

INFORMS Journal on Computing2026https://doi.org/10.1287/ijoc.2024.0914.cdarticle
AJG 3ABDC A
Weight
0.37

What the paper says

The software and data in this repository are a snapshot of the software and data that were used in the research reported on in the paper Efficient input uncertainty quantification for ratio estimator by L. He, M.B. Feng and E. Song. The snapshot is based on this SHA in the development repository.

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https://doi.org/https://doi.org/10.1287/ijoc.2024.0914.cd

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@article{linyun2026,
  title        = {{Code and Data Repository for Efficient input uncertainty quantification for ratio estimator}},
  author       = {Linyun He},
  journal      = {INFORMS Journal on Computing},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1287/ijoc.2024.0914.cd},
}

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Code and Data Repository for Efficient input uncertainty quantification for ratio estimator

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Evidence weight

0.37

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

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.