Factor-Adjusted Model Averaging
提出一种针对高维回归中高度相关协变量的模型平均方法，利用因子结构分解协变量，证明渐近最优性和一致性，数值实验和实际数据分析表现良好。
We propose a model averaging method for high-dimensional regression with highly correlated covariates. We use a factor structure to model the covariate dependence, allowing the covariates to be decomposed into two uncorrelated or weakly correlated latent components: common factors and idiosyncratic components. The number of common factors is allowed to diverge. We average estimators from factor-adjusted candidate models with augmented predictors composed of estimated common factors and idiosyncratic components. We prove the asymptotic optimality in the sense of achieving the lowest squared loss and the consistency when correctly specified models exist in the model space. Numerical experiments and a real-data analysis illustrate the promising performance of the proposed method.