Penalized semiparametric Cox regression model on XGBoost and random survival forests-Reference-Cited by-同舟云学术

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Penalized semiparametric Cox regression model on XGBoost and random survival forests

Published:2021-05-17 Issue: Volume: Page:1-12
ISSN:0361-0918
Container-title:Communications in Statistics - Simulation and Computation
language:en
Short-container-title:Communications in Statistics - Simulation and Computation

Author:

Wang Yating¹^ORCID,Su Jinxia¹^ORCID,Zhao Xuejing¹^ORCID

Affiliation:

1. School of Mathematics and Statistics, Lanzhou University, Lanzhou, China

Funder

National Natural Science Foundation of China

Scientific Research Foundation

Ministry of Education of China

Publisher

Informa UK Limited

Subject

Modeling and Simulation,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/03610918.2021.1926505

Reference11 articles.

1. Artificial neural networks improve the accuracy of cancer survival prediction

2. Chen, T., and C. Guestrin. 2016. Xgboost. A scalable tree boosting system. Proceedings of the 22nd Acm sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–94.

3. Regression Models and Life-Tables

4. Boosting in Cox regression: a comparison between the likelihood-based and the model-based approaches with focus on the R-packages CoxBoost and mboost

5. Survival trees for interval-censored survival data

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