A fast adaptive Lasso for the cox regression via safe screening rules. Issue 14 (22nd September 2021)

• Record Type: Journal Article
• Authors: Zhang, Zhuan; Shen, Zhenyuan; Wang, Hong; Ng, Shu Kay
• Is Part Of: Journal of statistical computation and simulation. Volume 91:Issue 14(2021)
• Page Start: 3005
• Page End: 3027
• Publication Date: 2021-09-22
• Subjects: Adaptive Lasso -- cox model -- high-dimensional survival data -- safe feature elimination screening; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2021.1914043
• Languages: English
• ISSNs: 0094-9655

Abstract

Abstract : Some interesting recent studies have shown that safe feature elimination screening algorithms are useful alternatives in solving large scale and/or ultra-high-dimensional Lasso-type problems. However, to the best of our knowledge, the plausibility of adapting the safe feature elimination screening algorithm to survival models is rarely explored. In this study, we first derive the safe feature elimination screening rule for adaptive Lasso Cox model. Then, using both simulated and real-world datasets, we demonstrate that the resulting algorithm can outperform Lasso Cox and adaptive Lasso Cox prediction methods in terms of its predictive performance. In addition to its good predictive performance, we illustrate that the proposed algorithm has a key computational advantage over the above competing methods in terms of computation efficiency.