Abstract

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.

摘要

我们提出了一个神经网络模型，用于估计满足非交叉属性的多个条件分位数。受线性非交叉分位数回归的启发，我们提出了一种具有不等式约束的非交叉分位数神经网络模型。特别是，为了使用一阶优化方法，我们开发了一种新算法来拟合所提出的模型。该算法给出了一个近乎最优的解决方案，而没有需要多项式计算时间的投影梯度步骤。我们将我们提出的模型的性能与现有的神经网络模型在模拟和真实降水数据上的性能进行了比较。本文的补充材料可在线获取。