Survival Regression with Accelerated Failure Time Model in XGBoost

DOI10.1080/10618600.2022.2067548OpenAlexW3035701850MaRDI QIDQ5057266

Hyunsu Cho, Toby Hocking, Avinash Barnwal

Publication date: 16 December 2022

Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2006.04920

zbMATH Keywords

survival analysisgradient boostingGPU computingopen sourceXGBoost

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (1)

A boosting first-hitting-time model for survival analysis in high-dimensional settings

Uses Software

• Scikit
• aftgee
• XGBoost
• LightGBM
• CatBoost
• pycox
• scikit-survival
• Optuna
• LambdaMART

Cites Work

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• Greedy function approximation: A gradient boosting machine.
• Random survival forests
• Variable selection for survival data with a class of adaptive elastic net techniques
• Boosting algorithms: regularization, prediction and model fitting
• Additive logistic regression: a statistical view of boosting. (With discussion and a rejoinder by the authors)
• Customer attrition analysis for financial services using proportional hazard models
• Nonparametric Estimation from Incomplete Observations
• Linear regression with censored data
• Least squares regression with censored data

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