预测误差框架下参数估计的渐近行为

Caiyun Chen, Zhibin Yan
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引用次数: 0

摘要

在预测误差法中,已知当输入输出数据的长度趋于无穷时,判据函数序列在参数内均匀收敛,其概率为1。当极限函数准则的极小点不唯一时,一般不能保证参数估计的收敛性。两种情况是有区别的。情形一是极限函数准则的极小点的集合是一个连续体,情形二是这个集合的点是孤立的。通过实例和理论分析,给出了不同情况下参数估计渐近性的一些有趣现象。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Asymptotical behavior of parameter estimation in prediction error framework
In prediction error method, it is known that the sequence of the criterion function converges uniformly in the parameter with probability one as the length of the input-output data tends to infinity. When the minimizing points of the limiting function criterion are not unique, the convergence of parameter estimation is not guaranteed in general. Two cases are distinguished. The case one is that the set of the minimizing points of the limiting function criterion is a continuum, and the case two is that the points of that set are isolated. Some interesting phenomena relating to the different cases are shown about the asymptotical behavior of parameter estimation through examples and theoretical analysis.
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