Author:
Kopper Philipp,Wiegrebe Simon,Bischl Bernd,Bender Andreas,Rügamer David
Publisher
Springer International Publishing
Reference45 articles.
1. Alaa, A.M., van der Schaar, M.: Deep multi-task gaussian processes for survival analysis with competing risks. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 2326–2334. Curran Associates Inc. (2017)
2. Argyropoulos, C., Unruh, M.L.: Analysis of time to event outcomes in randomized controlled trials by generalized additive models. PLoS ONE 10(4), e0123784 (2015)
3. Bender, A., Groll, A., Scheipl, F.: A generalized additive model approach to time-to-event analysis. Statist. Model. 18(3–4), 299–321 (2018)
4. Bender, A., Rügamer, D., Scheipl, F., Bischl, B.: A general machine learning framework for survival analysis. In: Hutter, F., Kersting, K., Lijffijt, J., Valera, I. (eds.) ECML PKDD 2020. LNCS (LNAI), vol. 12459, pp. 158–173. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-67664-3_10
5. Bender, A., Scheipl, F.: pammtools: piece-wise exponential additive mixed modeling tools. arXiv preprint arXiv:1806.01042 (2018)
Cited by
6 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献