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Deep learning for the joint analysis of item-level longitudinal and survival data

Jeffrey Lin & Sheng Luo

Journal of Applied Statistics2026https://doi.org/10.1080/02664763.2026.2625122article
AJG 2ABDC B
Weight
0.50

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https://doi.org/https://doi.org/10.1080/02664763.2026.2625122

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@article{jeffrey2026,
  title        = {{Deep learning for the joint analysis of item-level longitudinal and survival data}},
  author       = {Jeffrey Lin & Sheng Luo},
  journal      = {Journal of Applied Statistics},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1080/02664763.2026.2625122},
}

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Deep learning for the joint analysis of item-level longitudinal and survival data

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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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