论文标题
嵌套模型在溶液路径上平均高维线性回归
Nested Model Averaging on Solution Path for High-dimensional Linear Regression
论文作者
论文摘要
我们研究了在解决方案路径上的嵌套模型平均方法，以解决高维线性回归问题。特别是，我们建议将模型平均值与高维线性回归的解决方案路径上的正则化估计器（例如Lasso和Slope）相结合。在仿真研究中，我们首先对预测器排序对嵌套模型平均行为的影响进行系统研究，然后表明，嵌套模型与套索和斜率平均，并且与其他竞争方法相比，包括不可避免的套索和斜率，以及最佳选择的调谐参数。关于预测美国人均暴力犯罪的真实数据分析表明，与拉索平均嵌套模型的出色表现。
We study the nested model averaging method on the solution path for a high-dimensional linear regression problem. In particular, we propose to combine model averaging with regularized estimators (e.g., lasso and SLOPE) on the solution path for high-dimensional linear regression. In simulation studies, we first conduct a systematic investigation on the impact of predictor ordering on the behavior of nested model averaging, then show that nested model averaging with lasso and SLOPE compares favorably with other competing methods, including the infeasible lasso and SLOPE with the tuning parameter optimally selected. A real data analysis on predicting the per capita violent crime in the United States shows an outstanding performance of the nested model averaging with lasso.
