Optimization of algorithms for estimating parameters of stochastic systems in conditions of indeterminacy
type: Item
instance of: scholarly article
title: Optimization of algorithms for estimating parameters of stochastic systems in conditions of indeterminacy
zbMATH Open document ID: 0587.93060
published in: Automation and Remote Control
publication date: 1985-01-01T00:00:00Z
review text: The problem of estimating the parameters of a vector regression model with correlated measurement errors is considered. A general estimation method is proposed, whereby, given imperfect information about the correlation structure of the measurement errors, an adaptive estimate can be obtained for the unknown parameters, which is asymptotically not less accurate than the best linear unbiased estimate. The statistical properties of the estimate are examined, and the results of numerical experiments are quoted.
Mathematics Subject Classification ID: 93E10
Mathematics Subject Classification ID: 62F12
Mathematics Subject Classification ID: 93E25
Mathematics Subject Classification ID: 62M10
zbMATH DE Number: 3943686
zbMATH Keywords: vector regression model
zbMATH Keywords: imperfect information
zbMATH Keywords: adaptive estimate
author: Alexei R. Pankov
MaRDI profile type: Publication
description: scientific article; zbMATH DE number 3943686