Back to Search
Start Over
Sparse Laplacian shrinkage for nonparametric transformation survival model.
- Source :
-
Communications in Statistics: Theory & Methods . 2023, Vol. 52 Issue 20, p7184-7205. 22p. - Publication Year :
- 2023
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 03610926
- Volume :
- 52
- Issue :
- 20
- Database :
- Academic Search Index
- Journal :
- Communications in Statistics: Theory & Methods
- Publication Type :
- Academic Journal
- Accession number :
- 170393363
- Full Text :
- https://doi.org/10.1080/03610926.2022.2042025