基于神经元网络的条件分位数估计的收敛速度(英文)

The Convergent Rates of Estimation of Conditional Quantiles Using Artificial Neural

本文我们给出了基于神经元网络的随机过程的条件分位数的均方收敛速度.无论是在独立同分布情况下还是在平稳混合(α-混合β-混合)的情况下,我们都给出了相应的结果.结果与基于神经元网络的回归估计的收敛速度相同.采用的技术同Zhang(1998)一致.

In this paper, we give the mean square convergence rates of conditional quantile estimators based on single hidden layer feed forward networks. Our results are formulated both for independent identically distributed (i.i.d.) random variables and for stationary mixing processes (α-mixing and β-mixing). It turns out that the rates are the same as those for regression using neural networks. We use the same techniques as in Zhang (1998).