基于广义SELO惩罚的高维变量选择
HIGH-DIMENSIONAL VARIABLE SELECTION WITH THE GENERALIZED SELO PENALTY

本文考虑高维线性模型中的变量选择和参数估计.提出了一种广义的SELO方法求解惩罚最小二乘问题.一种坐标下降算法结合调节参数的一种连续化策略和高维BIC被用来计算相应的GSELO-PLS估计.模拟研究和实际数据分析显示了提出方法的良好表现.
In this paper, we consider the variable selection and parameter estimation in high-dimensional linear models. We propose a generalized SELO (GSELO) method for solving the penalized least-squares (PLS) problem. A coordinate descent algorithm coupled with a continuation strategy and high-dimensional BIC on the tuning parameter are used to compute corresponding GSELO-PLS estimators. Simulation studies and a real data analysis show the good performance of the proposed method