摘要
结合截面最小二乘估计思想,构造了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。由于部分线性模型LASSO解仍为线性优化问题,因此容易实现。在一定条件下,本文还研究了参数估计量的相合性和渐近正态性。最后通过蒙特卡洛模拟研究了变量选择方法的小样本性质。
Based on the profile least squares method, the LASSO penalty profile least squares estimator is constructed, and the choices of penalty parameter and bandwidth are also discussed. Because the optimization problem is linear, it can be easily implemented. Under some regular conditions, the consistency and asymptotic normality of the estimator for parameter component are investigated. Finally, Monte Carlo simulation studies are conducted to assess the finite sample performance of the proposed variable selection procedures.
出处
《山东大学学报(理学版)》
CAS
CSCD
北大核心
2012年第3期93-97,共5页
Journal of Shandong University(Natural Science)