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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.

the scientific world journal/thescientificworldjournal

Adaptive<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:msub><mml:mrow><mml:mtext>L</mml:mtext></mml:mrow><mml:mrow><mml:mn>1</mml:mn><mml:mo>/</mml:mo><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:math>Shooting Regularization Method for Survival Analysis Using Gene Expression Data

AdaptiveL1/2Shooting Regularization Method for Survival Analysis Using Gene Expression Data