Avtomat. i Telemekh., 2005, Issue 1, Pages 100–117
This article is cited in 1 scientific paper (total in 1 paper)
Adaptive and Robust Systems
Convergence of the least-squares method with a polynomial regularizer for the infinite-dimensional autoregression equation
A. E. Barabanov, Yu. R. Gel'
Saint-Petersburg State University
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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Automation and Remote Control, 2005, 66:1, 92–107
Presented by the member of Editorial Board: B. M. Miller
A. E. Barabanov, Yu. R. Gel', “Convergence of the least-squares method with a polynomial regularizer for the infinite-dimensional autoregression equation”, Avtomat. i Telemekh., 2005, no. 1, 100–117; Autom. Remote Control, 66:1 (2005), 92–107
Citation in format AMSBIB
\by A.~E.~Barabanov, Yu.~R.~Gel'
\paper Convergence of the least-squares method with a~polynomial regularizer for the infinite-dimensional autoregression equation
\jour Avtomat. i Telemekh.
\jour Autom. Remote Control
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This publication is cited in the following articles:
Gel Yu.R., Barabanov A., “Strong consistency of the regularized least-squares estimates of infinite autoregressive models”, J Statist Plann Inference, 137:4 (2007), 1260–1277
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