Abstract
We propose a new bi-level feature selection method for high dimensional accelerated failure time models by formulating the models to a single index model. The method yields sparse solutions at both the group and individual feature levels along with an expedient algorithm, which is computationally efficient and easily implemented. We analyze a genomic dataset for an illustration, and present a simulation study to show the finite sample performance of the proposed method.
Acknowledgements
The authors thank Associate Editor, Dr. Korbinian Strimmer, and a referee for their constructive suggestions and comments that have substantially improved an earlier version of this paper. Liang’s research was partially supported by NSF grant DMS-1620898.
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Supplementary Material
The online version of this article offers supplementary material (DOI: https://doi.org/10.1515/sagmb-2019-0016).
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