部分线性模型的Adaptive LASSO变量选择

Variable Selection for Partially Linear Models via Adaptive LASSO

部分线性模型是一类常用的半参数统计模型,本文对部分线性模型的adaptive LASSO参数估计及变量选择方法进行了研究.首先结合截面最小二乘思想和adaptive LASSO估计方法,构造了adaptive LASSO惩罚截面最小二乘估计,并研究了惩罚参数和窗宽的选择问题.理论上研究了在一定条件下估计量的相合性和渐近正态性,证明adaptive LASSO估计具有oracle性质.该估计方法便于计算.最后通过模拟研究了估计量的小样本性质,结果表明变量选择和参数估计效果良好.

Partially linear model is a class of commonly used semiparametric models, this paper focus on variable selection and parameter estimation for partially linear models via adaptive LASSO method. Firstly, based on profile least squares and adaptive LASSO method, the adaptive LASSO estimator for partially linear models are constructed, and the selections of penalty parameter and bandwidth are discussed. Under some regular conditions, the consistency and asymptotic normality for the estimator are investigated, and it is proved that the adaptive LASSO estimator has the oracle properties. The proposed method can be easily implemented. Finally a Monte Carlo simulation study is conducted to assess the finite sample performance of the proposed variable selection procedure, results show the adaptive LASSO estimator behaves well.