Penalized Estimating Functions and Variable Selection in Semiparametric Regression Models
半参数回归模型中的惩罚估计函数与变量选择
We propose a general strategy for variable selection in semiparametric regression models by penalizing appropriate estimating functions. Important applications include semiparametric linear regression with censored responses and semiparametric regression with missing predictors. Unlike the existing penalized maximum likelihood estimators, the proposed penalized estimating functions may not pertain to the derivatives of any objective functions and may be discrete in the…