101
Views
0
CrossRef citations to date
0
Altmetric
Articles

Sparse Laplacian shrinkage for nonparametric transformation survival model

&
Pages 7184-7205 | Received 10 Jul 2021, Accepted 09 Feb 2022, Published online: 07 Mar 2022

References

  • Agarwal, A., S. Negahban, and M. J. Wainwright. 2012. Fast global convergence of gradient methods for high-dimensional statistical recovery. The Annals of Statistics 40 (5):2452–82. doi:10.1214/12-AOS1032.
  • Asad, A. S., A. J. Nicola Candia, N. Gonzalez, C. F. Zuccato, A. Abt, S. J. Orrillo, Y. Lastra, E. De Simone, F. Boutillon, V. Goffin, et al. 2019. Prolactin and its receptor as therapeutic targets in glioblastoma multiforme. Scientific Reports 9 (1):1–16. doi:10.1038/s41598-019-55860-x.
  • Bertsekas, D. P. 1999. Nonlinear programming. Belmont: Athena Scientific.
  • Cai, T., L. Tian, and L. J. Wei. 2005. Semiparametric box: Cox power transformation models for censored survival observations. Biometrika 92 (3):619–32. doi:10.1093/biomet/92.3.619.
  • Cavanagh, C., and R. P. Sherman. 1998. Rank estimators for monotonic index models. Journal of Econometrics 84 (2):351–81. doi:10.1016/S0304-4076(97)00090-0.
  • Chen, K., Z. Jin, and Z. Ying. 2002. Semiparametric analysis of transformation models with censored data. Biometrika 89 (3):659–68. doi:10.1093/biomet/89.3.659.
  • Clarke, F. H. 1990. Optimization and nonsmooth analysis. Philadelphia: Society for Industrial and Applied Mathematics.
  • Han, A. K. 1987. Non-parametric analysis of a generalized regression model. The maximum rank correlation estimator. Journal of Econometrics 35 (2–3):303–16. doi:10.1016/0304-4076(87)90030-3.
  • Hu, H., Z. Wang, M. Li, F. Zeng, K. Wang, R. Huang, H. Wang, F. Yang, T. Liang, H. Huang, et al. 2017. Gene expression and methylation analyses suggest DCTD as a prognostic factor in malignant glioma. Scientific Reports 7 (1):1–9. doi:10.1038/s41598-017-11962-y.
  • Huang, J., and S. Ma. 2010. Variable selection in the accelerated failure time model via the bridge method. Lifetime Data Analysis 16 (2):176–95. doi:10.1007/s10985-009-9144-2.
  • Huang, J., S. Ma, H. Li, and C.-H. Zhang. 2011. The sparse laplacian shrinkage estimator for high-dimensional regression. Annals of Statistics 39 (4):2021–46.
  • Huang, J., and C.-H. Zhang. 2008. Adaptive lasso for sparse high-dimensional. Statistica Sinica 18 (4):1603–18.
  • Johansson, P., C. Krona, S. Kundu, M. Doroszko, S. Baskaran, L. Schmidt, C. Vinel, E. Almstedt, R. Elgendy, L. Elfineh, et al. 2020. A patient-derived cell atlas informs precision targeting of glioblastoma. Cell Reports 32 (2):107897. doi:10.1016/j.celrep.2020.107897.
  • Khan, S., and E. Tamer. 2007. Partial rank estimation of duration models with general forms of censoring. Journal of Econometrics 136 (1):251–80. doi:10.1016/j.jeconom.2006.03.003.
  • Li, C., and H. Li. 2010. Variable selection and regression analysis for graph-structured covariates with an application to genomics. The Annals of Applied Statistics 4 (3):1498–516. doi:10.1214/10-AOAS332.
  • Lin, H., and H. Peng. 2013. Smoothed rank correlation of the linear transformation regression model. Computational Statistics & Data Analysis 57 (1):615–30. doi:10.1016/j.csda.2012.07.012.
  • Liu, Y., J. Xu, and G. Li. 2021. Sure joint feature screening in nonparametric transformation model for right censored data. Canadian Journal of Statistics 49 (2):549–65. doi:10.1002/cjs.11575.
  • Loh, P., and L. 2017. Statistical consistency and asymptotic normality for high-dimensional robust m-estimators. The Annals of Statistics 45 (2):866–96. doi:10.1214/16-AOS1471.
  • Loh, P. L., and M. J. Wainwright. 2015. Regularized M-estimators with nonconvexity: Statistical and algorithmic theory for local optima. Journal of Machine Learning Research 16 (1):559–616.
  • Mao, X. G., X. Y. Xue, L. Wang, X. Zhang, M. Yan, Y. Y. Tu, W. Lin, X. F. Jiang, H. G. Ren, W. Zhang, et al. 2013. CDH5 is specifically activated in glioblastoma stemlike cells and contributes to vasculogenic mimicry induced by hypoxia. Neuro-oncology 15 (7):865–79. doi:10.1093/neuonc/not029.
  • Negahban, S. N., P. Ravikumar, M. J. Wainwright, and B. Yu. 2012. A unified framework for high-dimensional analysis of m-estimators with decomposable regularizers. Statistical Science 27 (4):538–57. doi:10.1214/12-STS400.
  • Shi, X., Y. Huang, J. Huang, and S. Ma. 2018. A forward and backward stagewise algorithm for nonconvex loss functions with adaptive lasso. Computational Statistics & Data Analysis 124:235–51.
  • Shi, X., J. Liu, J. Huang, Y. Zhou, Y. Xie, and S. Ma. 2014. A penalized robust method for identifying gene-environment interactions. Genetic Epidemiology 38 (3):220–30. doi:10.1002/gepi.21795.
  • Shi, X., Q. Zhao, J. Huang, Y. Xie, and S. Ma. 2015. Deciphering the associations between gene expression and copy number alteration using a sparse double laplacian shrinkage approach. Bioinformatics (Oxford, England) 31 (24):3977–83. doi:10.1093/bioinformatics/btv518.
  • Song, X., S. Ma, J. Huang, and X. H. Zhou. 2007. A semiparametric approach for the nonparametric transformation survival model with multiple covariates. Biostatistics (Oxford, England) 8 (2):197–211. doi:10.1093/biostatistics/kxl001.
  • Sun, H., W. Lin, R. Feng, and H. Li. 2014. Network-regularized high-dimensional cox regression for analysis of genomic data. Statistica Sinica 24 (3):1433–59. doi:10.5705/ss.2012.317.
  • Tong, L., J. Li, J. Choi, A. Pant, Y. Xia, C. Jackson, P. Liu, L. Yi, E. Boussouf, M. Lim, et al. 2020. CLEC5A expressed on myeloid cells as a M2 biomarker relates to immunosuppression and decreased survival in patients with glioma. Cancer Gene Therapy 27 (9):669–79. doi:10.1038/s41417-019-0140-8.
  • Uno, H., T. Cai, M. J. Pencina, R. B. D'Agostino, and L. J. Wei. 2011. On the C-statistics for evaluating overall adequacy of risk prediction procedures with censored survival data. Statistics in Medicine 30 (10):1105–17. doi:10.1002/sim.4154.
  • Wang, L., B. Peng, J. Bradic, R. Li, and Y. Wu. 2020. A Tuning-free Robust and Efficient Approach to High-dimensional Regression. Journal of the American Statistical Association 115 (532):1700–14. doi:10.1080/01621459.2020.1840989.
  • Wu, M., Q. Zhang, and S. Ma. 2020. Structured gene-environment interaction analysis. Biometrics 76 (1):23–35. doi:10.1111/biom.13139.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.