Optimal Model Averaging of Mixed-Data Kernel-Weighted Spline Regressions
提出一种针对分类回归样条的频率学派模型平均方法，适用于混合数据预测变量和非嵌套异方差候选模型，证明了渐近最优性并开发了平均后推断理论，实证表明优于多种流行模型选择准则。
Model averaging has a rich history dating from its use for combining forecasts from time-series models (Bates and Granger) and presents a compelling alternative to model selection methods. We propose a frequentist model averaging procedure defined over categorical regression splines (Ma, Racine, and Yang) that allows for mixed-data predictors, as well as nonnested and heteroscedastic candidate models. We demonstrate the asymptotic optimality of the proposed model averaging estimator, and develop a post-averaging inference theory for it. Theoretical underpinnings are provided, finite-sample performance is evaluated, and an empirical illustration reveals that the method is capable of outperforming a range of popular model selection criteria in applied settings. An R package is available for practitioners (Racine).