Estimating the generalized autoregression model parameters for unknown noise distribution

被引：1
|
作者：
Malyarenko, A. A. [1 ]
机构：
[1] Tomsk VV Kuibyshev State Univ, Tomsk 634050, Russia
来源：
AUTOMATION AND REMOTE CONTROL | 2010年 / 71卷 / 02期
基金：
俄罗斯基础研究基金会;
关键词：
Remote Control; Noise Process; Strong Consistency; Noise Distribution; Fourth Moment;
D O I：
10.1134/S0005117910020104
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
We solve the problem of estimating the autoregressive parameters of a nonlinear stable stochastic process with discrete time of the AR(p)/ARCH(p) type with unknown ARCH(p) process parameters. For the AR(1)/ARCH(1) model, we solve the estimation problem for all unknown process parameters, i.e., the autoregression parameter and two parameters of the noise process ARCH(1). We assume that the noise distributions are unknown. We show that the least square estimates are strongly consistent.
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页码：291 / 302
页数：12
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