A fast adaptive Lasso for the cox regression via safe screening rules
Author(s)
Zhang, Z
Shen, Z
Wang, H
Ng, SK
Griffith University Author(s)
Year published
2021
Metadata
Show full item recordAbstract
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 ...
View more >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.
View less >
View more >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.
View less >
Journal Title
Journal of Statistical Computation and Simulation
Funder(s)
ARC
Grant identifier(s)
DP170100907
Note
This publication has been entered in Griffith Research Online as an advanced online version.
Subject
Statistics
Applied economics
Econometrics