Linear regression and filtering under nonstandard assumptions (arbitrary noise)

被引：25
|
作者：
Granichin, O [1 ]
机构：
[1] St Petersburg State Univ, Dept Math & Mech, St Petersburg 198904, Russia
来源：
IEEE TRANSACTIONS ON AUTOMATIC CONTROL | 2004年 / 49卷 / 10期
关键词：
filtering; linear regression; parameter estimation; prediction; randomized algorithm;
D O I：
10.1109/TAC.2004.835585
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This note is devoted to parameter estimation in linear regression and filtering, where the observation noise does not possess any "nice" probabilistic properties. In particular, the noise might have an "Unknown-but-bounded" deterministic nature. The basic assumption is that the model regressors (inputs) are random. Optimal rates of convergence for the modified stochastic approximation and least-squares algorithms are established under some weak assumptions. Typical behavior of the algorithms in the presence of such deterministic noise is illustrated by numerical examples.
引用
收藏
页码：1830 / 1835
页数：6
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