Sparse Laplacian shrinkage for nonparametric transformation survival model

Xiao Zhang and Yiming Liu

Communications in Statistics - Theory and Methods, 2023, vol. 52, issue 20, 7184-7205

Abstract: The rank estimation is an effective inference method for the nonparametric transformation model. This approach avoids any nonparametric estimation about the transformation function and can be applied to the high-dimensional censored data. However, most existing methods do not utilize the potential correlation structures among predictors. In order to incorporate such priori information, we propose a penalized smoothed partial rank with sparse Laplacian shrinkage (PSPRL) method and develop a forward and backward stagewise with sparse Laplacian shrinkage (LFabs) algorithm to compute the estimator. The non-asymptotic bound and algorithm properties are established. Simulation results show that the proposed method outperforms the competing alternatives with better variable selection and prediction. We apply our method to a glioblastoma gene expression study to further demonstrate the advantages.

Date: 2023
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DOI: 10.1080/03610926.2022.2042025

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