Projection‐based and cross‐validated estimation in high‐dimensional Cox model
提出一种投影交叉验证方法，在Cox回归模型中估计低维参数，同时处理高维干扰参数，并给出假设检验方法，模拟显示比现有方法更有效。
Abstract We propose a projection‐based cross‐validation method for estimating a low‐dimensional parameter in the presence of a high‐dimensional nuisance parameter in the Cox regression model. We show that the proposed estimator is asymptotically normal, which enables us to conduct hypothesis test for the parameter of interest with high‐dimensional nuisance parameters. Three decision rules are presented to avoid the influence of random splitting of samples. Simulation studies indicate that our method is more powerful than that of Fang et al. (2017, JRSSB ) when the coefficients of predictors are high‐dimensional and not very sparse. As an illustrative example, we apply our procedure to a breast cancer study.