Proximity Based Survival Analysis
Reason this release was yanked:
Currently the package is unavailable, due to some internal problem we are upgrading, will be available soon
Project description
PBSA : Proximity Based Survival Analysis
Proximity Based Survival Analysis are algorithms, designed for survival prediction using proximity information. The k-NN survival, Random Survival Forest, Kernel Survival are some examples of proximity based survival analysis. While this package tends to provide those algorithms later, currently the package provides the following algorithms:
- COBRA Survival
For now other algorithms are taken from scikit-survival and np_survival to provide as a base learner for the ensemble algorithms.
installation
pip install proxsurv
The documentation is available at https://pbsa.readthedocs.io/en/latest/
References
- Goswami, Rahul & Dey, Arabin. (2023). Area-norm COBRA on Conditional Survival Prediction. The paper explores a different variation of combined regression strategy to calculate the conditional survival function. We use regression based weak learners to create the proposed ensemble technique. The proposed combined regression strategy uses proximity measure as area between two survival curves. The proposed model shows a construction which ensures that it performs better than the Random Survival Forest. The paper discusses a novel technique to select the most important variable in the combined regression setup. We perform a simulation study to show that our proposition for finding relevance of the variables works quite well. We also use three real-life datasets to illustrate the model.
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