Variable selection and estimation in proportional hazards models with additive relative risk is considered. Both objectives are achieved using a penalized partial likelihood with a group nonconcave penalty. Oracle properties of the estimator are demonstrated, when the dimensionality is allowed to be larger than sample size. To deal with the computational challenges when p>n, an active-set-type algorithm is proposed. Finally, the method is illustrated with simulation examples and a real microarray study. © 2013 Elsevier B.V. All rights reserved.
CITATION STYLE
Lian, H., Li, J., & Hu, Y. (2013). Shrinkage variable selection and estimation in proportional hazards models with additive structure and high dimensionality. Computational Statistics and Data Analysis, 63, 99–112. https://doi.org/10.1016/j.csda.2013.02.003
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