论文标题
期望神经网络的渐近理论
Asymptotic Theory of Expectile Neural Networks
论文作者
论文摘要
神经网络正在成为应用程序中越来越重要的工具。但是，神经网络并未在统计遗传学中广泛使用。在本文中，我们提出了一种新的神经网络方法，称为Expectile神经网络。当参数的大小太大时，标准最大似然过程可能无法正常工作。我们使用筛子方法来约束参数空间。我们在非参数回归框架下证明了它的一致性和正态性。
Neural networks are becoming an increasingly important tool in applications. However, neural networks are not widely used in statistical genetics. In this paper, we propose a new neural networks method called expectile neural networks. When the size of parameter is too large, the standard maximum likelihood procedures may not work. We use sieve method to constrain parameter space. And we prove its consistency and normality under nonparametric regression framework.
