On Sequential Least Squares Estimates of Autoregressive Parameters

Leonid Galtchouk; Victor Konev

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On Sequential Least Squares Estimates of Autoregressive Parameters

机译：自回归参数的序列最小二乘估计

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摘要

For estimation of the parameters of a stable autoregressive process AR(p) by the least squares method, the paper proposes to use a particular stopping rule that essentially depends on the behavior of the minimal eigenvalue of the observed Fisher information matrix. The upper bounds for the mean square error of estimation are derived in the cases of known and unknown variances of the noise. Asymptotic formulas for the mean of the stopping time are given in both cases.

机译：为了通过最小二乘法估计稳定的自回归过程AR（p）的参数，本文提出使用特定的停止规则，该规则基本上取决于所观察的Fisher信息矩阵的最小特征值的行为。在噪声方差已知和未知的情况下，得出估计均方误差的上限。在两种情况下均给出了停止时间平均值的渐近公式。

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来源
《Sequential analysis》 |2005年第4期|p.335-364|共30页
作者
Leonid Galtchouk; Victor Konev;
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作者单位

IRMA, Departement de Mathematiques, Universite de Strasbourg, 7, rue Rene Descartes, F-67084, Strasbourg Cedex, France;

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原文格式 PDF
正文语种 eng
中图分类统计学;
关键词
autoregression; guaranteed precision; least-squares estimates; sequential procedure; stochastic regression; stopping times;

机译：自回归;保证的精度;最小二乘估计;顺序程序;随机回归;停止时间;

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On Sequential Least Squares Estimates of Autoregressive Parameters

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