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Communication-efficient distributed composite quantile regression via convolution smoothing and poisson subsampling

Jun Jin & Qinghan Liang

Statistical Papers2026https://doi.org/10.1007/s00362-025-01787-7article
ABDC B
Weight
0.37

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https://doi.org/https://doi.org/10.1007/s00362-025-01787-7

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@article{jun2026,
  title        = {{Communication-efficient distributed composite quantile regression via convolution smoothing and poisson subsampling}},
  author       = {Jun Jin & Qinghan Liang},
  journal      = {Statistical Papers},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1007/s00362-025-01787-7},
}

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Communication-efficient distributed composite quantile regression via convolution smoothing and poisson subsampling

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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.