- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
1 Online-Ressource.
- Language
-
Englisch
- Bibliographic citation
-
Stable Cox regression for survival analysis under distribution shifts ; volume:6 ; number:12 ; day:13 ; month:12 ; year:2024 ; pages:1525-1541 ; date:12.2024
Nature machine intelligence ; 6, Heft 12 (13.12.2024), 1525-1541, 12.2024
- Creator
-
Fan, Shaohua
Xu, Renzhe
Dong, Qian
He, Yue
Chang, Cheng
Cui, Peng
- Contributor
-
SpringerLink (Online service)
- DOI
-
10.1038/s42256-024-00932-5
- URN
-
urn:nbn:de:101:1-2502282112262.544920943179
- Rights
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
15.08.2025, 7:26 AM CEST
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Fan, Shaohua
- Xu, Renzhe
- Dong, Qian
- He, Yue
- Chang, Cheng
- Cui, Peng
- SpringerLink (Online service)
Other Objects (12)
Pathway-gene identification for pancreatic cancer survival via doubly regularized Cox regression
Identifying shifts between two regression curves
Testing under a modified Cox regression model
Comparison of the cox regression to machine learning in predicting the survival of anaplastic thyroid carcinoma
A Note on Implementing Box-Cox Quantile Regression
A Note on Implementing Box-Cox Quantile Regression
A note on implementing Box-Cox quantile regression
Cox regression can be collapsible and Aalen regression can be non-collapsible
Optimal stable Ornstein–Uhlenbeck regression
Modeling survival data : extending the cox model
Correction: Comparison of the cox regression to machine learning in predicting the survival of anaplastic thyroid carcinoma
Predicting survival of advanced laryngeal squamous cell carcinoma: comparison of machine learning models and Cox regression models
Pathway-gene identification for pancreatic cancer survival via doubly regularized Cox regression
Identifying shifts between two regression curves
Testing under a modified Cox regression model
Comparison of the cox regression to machine learning in predicting the survival of anaplastic thyroid carcinoma
A Note on Implementing Box-Cox Quantile Regression
A Note on Implementing Box-Cox Quantile Regression
A note on implementing Box-Cox quantile regression
Cox regression can be collapsible and Aalen regression can be non-collapsible
Optimal stable Ornstein–Uhlenbeck regression
Modeling survival data : extending the cox model
Correction: Comparison of the cox regression to machine learning in predicting the survival of anaplastic thyroid carcinoma
Predicting survival of advanced laryngeal squamous cell carcinoma: comparison of machine learning models and Cox regression models
Pathway-gene identification for pancreatic cancer survival via doubly regularized Cox regression
Identifying shifts between two regression curves
Testing under a modified Cox regression model
Comparison of the cox regression to machine learning in predicting the survival of anaplastic thyroid carcinoma
A Note on Implementing Box-Cox Quantile Regression
A Note on Implementing Box-Cox Quantile Regression
A note on implementing Box-Cox quantile regression
Cox regression can be collapsible and Aalen regression can be non-collapsible
Optimal stable Ornstein–Uhlenbeck regression
Modeling survival data : extending the cox model
Correction: Comparison of the cox regression to machine learning in predicting the survival of anaplastic thyroid carcinoma