Learning Multiple Quantiles With Neural Networks
提出一种满足非交叉性质的多条件分位数神经网络模型，并开发了无需投影梯度步的快速算法，在模拟和真实降水数据上表现优于现有模型。
We present a neural network model for estimation of multiple conditional quantiles that satisfies the noncrossing property. Motivated by linear noncrossing quantile regression, we propose a noncrossing quantile neural network model with inequality constraints. In particular, to use the first-order optimization method, we develop a new algorithm for fitting the proposed model. This algorithm gives a nearly optimal solution without the projected gradient step that requires polynomial computation time. We compare the performance of our proposed model with that of existing neural network models on simulated and real precipitation data. Supplementary materials for this article are available online.