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Conference papers

Experimental Comparison of Semi-parametric, Parametric, and Machine Learning Methods for Time-to-Event Analysis Through the IPEC Score

Abstract : In this paper, we make an experimental comparison of semi-parametric (Cox proportional hazards model, Aalen additive model), parametric (Weibull AFT model), and machine learning methods (Random Survival Forest, Gradient Boosting Cox proportional hazards loss, DeepSurv) through the IPEC score on three different datasets (PBC, GBCSG2 and TLCM).
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Conference papers
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https://hal.inria.fr/hal-03221512
Contributor : Chung Shue Chen Connect in order to contact the contributor
Submitted on : Saturday, May 8, 2021 - 11:14:19 PM
Last modification on : Wednesday, May 4, 2022 - 1:32:02 PM
Long-term archiving on: : Monday, August 9, 2021 - 6:17:22 PM

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  • HAL Id : hal-03221512, version 1

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Camila Fernández, Chung Shue Chen, Pierre Gaillard, Alonso Silva. Experimental Comparison of Semi-parametric, Parametric, and Machine Learning Methods for Time-to-Event Analysis Through the IPEC Score. SFdS 2020 - 52èmes Journées de Statistiques de la Société Française de Statistique, Jun 2021, Nice, France. pp.1-6. ⟨hal-03221512⟩

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