Adaptive L(1/2) Shooting Regularization Method for Survival Analysis Using Gene Expression Data

A new adaptive L(1/2) shooting regularization method for variable selection based on the Cox's proportional hazards mode being proposed. This adaptive L(1/2) shooting algorithm can be easily obtained by the optimization of a reweighed iterative series of L(1) penalties and a shooting strategy o...

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Detalles Bibliográficos
Autores principales: Liu, Xiao-Ying, Liang, Yong, Xu, Zong-Ben, Zhang, Hai, Leung, Kwong-Sak
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2013
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3876878/
https://www.ncbi.nlm.nih.gov/pubmed/24453861
http://dx.doi.org/10.1155/2013/475702
Descripción
Sumario:A new adaptive L(1/2) shooting regularization method for variable selection based on the Cox's proportional hazards mode being proposed. This adaptive L(1/2) shooting algorithm can be easily obtained by the optimization of a reweighed iterative series of L(1) penalties and a shooting strategy of L(1/2) penalty. Simulation results based on high dimensional artificial data show that the adaptive L(1/2) shooting regularization method can be more accurate for variable selection than Lasso and adaptive Lasso methods. The results from real gene expression dataset (DLBCL) also indicate that the L(1/2) regularization method performs competitively.

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