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

作　　者：张建同[1] 

机构地区：[1]同济大学经济与管理学院,上海200092

出　　处：《应用概率统计》2004年第1期37-46,共10页Chinese Journal of Applied Probability and Statistics

基　　金：Project sponsored by the Scientific Research Foundation for the Returned Overseas Chinese Scholars,State Education Ministry.

摘　　要：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).

关 键 词：条件分位数估计 混合过程 神经元网络 收敛速度 

分 类 号：O212.7[理学—概率论与数理统计]