Lassoed boosting and linear prediction in the equities market
提出一种两阶段线性回归方法：先用套索筛选变量，再用最小二乘增强重新估计系数。模拟显示其性能与松弛套索相当且模型更稀疏，在股票收益预测中均方预测误差最小。
We consider a two-stage estimation method for linear regression. First, it uses the lasso in Tibshirani to screen variables and, second, re-estimates the coefficients using the least-squares boosting method in Friedman on every set of selected variables. Based on the large-scale simulation experiment in Hastie, Tibshirani, and Tibshirani, lassoed boosting performs as well as the relaxed lasso in Meinshausen and, under certain scenarios, can yield a sparser model. Applied to predicting equity returns, lassoed boosting gives the smallest mean-squared prediction error compared to several other methods.