Variable selection in high-dimensional linear models: partially faithful distributions and the PC-simple algorithm
高维线性模型的变量选择：部分忠实性与 PC-simple 算法
We consider variable selection in high-dimensional linear models where the number of covariates greatly exceeds the sample size. We introduce the new concept of partial faithfulness and use it to infer associations between the covariates and the response. Under partial faithfulness, we develop a simplified version of the pc algorithm (Spirtes et al., 2000), which is computationally feasible even with thousands of covariates and provides consistent variable selection under…