SICA for Cox’s proportional hazards model with a diverging number of parameters |
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基金项目: | Supported by the National Natural Science Foundation of China(No.11171263) |
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摘 要: | The smooth integration of counting and absolute deviation (SICA) penalized variable selection procedure for high-dimensional linear regression models is proposed by Lv and Fan (2009). In this article, we extend their idea to Cox's proportional hazards (PH) model by using a penalized log partial likelihood with the SICA penalty. The number of the regression coefficients is allowed to grow with the sample size. Based on an approximation to the inverse of the Hessian matrix, the proposed method can be easily carried out with the smoothing quasi-Newton (SQN) algorithm. Under appropriate sparsity conditions, we show that the resulting estimator of the regression coefficients possesses the oracle property. We perform an extensive simulation study to compare our approach with other methods and illustrate it on a well known PBC data for predicting survival from risk factors.
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关 键 词: | 风险模型 Cox 比例 发散 线性回归模型 回归系数 一体化体系 选择程序 |
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