论文标题
完全的子集平均分位数回归
Complete Subset Averaging for Quantile Regressions
论文作者
论文摘要
我们提出了一种基于完全子集平均（CSA）的新型条件分位数预测方法，以进行分位数回归。所有正在考虑的模型都可能误指出，并且随着样本量的增加，回归器的尺寸变为无穷大。由于我们在完整的子集上的平均值，因此模型的数量比采用复杂的加权方案的通常模型平均方法要大得多。我们建议使用相等的重量，但根据保留的交叉验证方法选择完整子集的适当大小。在Lu and Su理论（2015）的基础上，我们研究了CSA的较大样本特性，并在Li（1987）的意义上显示了渐近优化性。我们通过蒙特卡洛模拟和经验应用来检查有限样本性能。
We propose a novel conditional quantile prediction method based on complete subset averaging (CSA) for quantile regressions. All models under consideration are potentially misspecified and the dimension of regressors goes to infinity as the sample size increases. Since we average over the complete subsets, the number of models is much larger than the usual model averaging method which adopts sophisticated weighting schemes. We propose to use an equal weight but select the proper size of the complete subset based on the leave-one-out cross-validation method. Building upon the theory of Lu and Su (2015), we investigate the large sample properties of CSA and show the asymptotic optimality in the sense of Li (1987). We check the finite sample performance via Monte Carlo simulations and empirical applications.
