In this paper, we investigate to use theL1/2 regularization method for variable selection based on the Cox's proportional hazards model. The L1/2 regularization method isa reweighed iterative algorithm with the adaptively weighted L1 penalty on regression coefficients. The algorithm of the L1/2 regularization method can be easily obtained by a series of L1 penalties. Simulation results based on standard artificial data show that the L1/2 regularization method can be more accurate for variable selection than Lasso and adaptive Lasso methods. The results from Primary Biliary Cirrhosis (PBC) dataset indicate the L 1/2 regularization method performs competitively. © 2012 Springer-Verlag.
CITATION STYLE
Liu, C., Liang, Y., Luan, X. Z., Leung, K. S., Chan, T. M., Xu, Z. B., & Zhang, H. (2012). Iterative L1/2 regularization algorithm for variable selection in the Cox proportional hazards model. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7332 LNCS, pp. 11–17). https://doi.org/10.1007/978-3-642-31020-1_2
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