• Corpus ID: 249017545

A scalable and flexible Cox proportional hazards model for high-dimensional survival prediction and functional selection

@inproceedings{Guo2022ASA,
  title={A scalable and flexible Cox proportional hazards model for high-dimensional survival prediction and functional selection},
  author={Boyi Guo and Nengjun Yi},
  year={2022}
}
Cox proportional hazards model is one of the most popular models in biomedical data analysis. There have been continuing efforts to improve the flexibility of such models for complex signal detection, for example, via additive functions. Nevertheless, the task to extend Cox additive models to accomodate high-dimensional data is nontrivial. When estimating additive functions, commonly used group sparse regularization may introduce excess smoothing shrinkage on additive functions, damaging… 

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The R Package BHAM: Fast and Scalable Bayesian Hierarchical Additive Model for High-dimensional Data

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