Convergence of the least-squares method with a polynomial regularizer for the infinite-dimensional autoregression equation

被引：0
作者：
A. E. Barabanov
Yu. R. Gel’
机构：
[1] St. Petersburg State University,
来源：
Automation and Remote Control | 2005年 / 66卷 / 1期
关键词：
Time Series; Mechanical Engineer; System Theory; Unknown Parameter; Autoregressive Model;
D O I：
10.1007/s10513-005-0009-1
中图分类号：
学科分类号：
摘要：
Consideration was given to the estimation of the unknown parameters of a stable infinite-dimensional autoregressive model from the observations of a random time series. The class of such models includes an autoregressive moving-average equation with a stable moving-average part. A modified procedure of the least-squares method was used to identify the unknown parameters. For the infinite-dimensional case, the estimates of the least-squares method were proved to be strong consistent. In addition, presented was a fact on convergence of the semimartingales that is of independent interest.
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页码：92 / 107
页数：15
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