On the Convergence of Least Squares Estimator for Nonlinear Autoregressive Models

被引：0

作者：

Liu, Zhaobo ^{[1
]}

Li, Chanying ^{[2
,3
]}

机构：

[1] Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen 518060, Peoples R China

[2] Chinese Acad Sci, Acad Math & Syst Sci, Key Lab Syst & Control, Beijing 100190, Peoples R China

[3] Univ Chinese Acad Sci, Sch Math Sci, Beijing 100049, Peoples R China

来源：

2021 PROCEEDINGS OF THE 40TH CHINESE CONTROL CONFERENCE (CCC) | 2021年

基金：

国家重点研发计划;

关键词：

Nonlinear Autoregressive Models; Strong Consistency; Least Squares; Harris Recurrent; CONSISTENCY;

D O I：

暂无

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 paper is concerned with the least squares estimator for a basic class of nonlinear autoregressive models, whose outputs are not necessarily to be ergodic. Several asymptotic properties of the least squares estimator have been established under mild conditions. These properties suggest the strong consistency of the least squares estimates in nonlinear autoregressive models which are not divergent.

引用

页码：1389 / 1394

页数：6

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