On the use of constraints in least squares estimation and control
type: Item
instance of: scholarly article
title: On the use of constraints in least squares estimation and control
zbMATH Open document ID: 1002.93064
DOI: 10.1016/S0005-1098(02)00029-8
author: Douglas G. Robertson
author: Jay H. Lee
published in: Automatica
publication date: 2002-09-05T00:00:00Z
review text: Using quadratic penalty functions, the authors examine the effect of constraints on estimation and control methods. The effect of such constraints on the statistical properties of least squares estimators is investigated, and the role of constraints in the formulation of maximum likelihood and maximum a posteriori estimators is explored. The potential for improved performance over unconstrained least squares is demonstrated by an example involving parameter estimation.
Mathematics Subject Classification ID: 93E24
Mathematics Subject Classification ID: 93E10
Mathematics Subject Classification ID: 90C20
zbMATH DE Number: 1797090
zbMATH Keywords: optimal estimation
zbMATH Keywords: non-Gaussian processes
zbMATH Keywords: constrained parameters
zbMATH Keywords: estimation algorithms
zbMATH Keywords: quadratic programming
zbMATH Keywords: maximum likelihood estimation
zbMATH Keywords: least squares estimators
zbMATH Keywords: performance
reviewed by: Andrew Dale
MaRDI profile type: Publication
cites work: Q4315010
cites work: Inequality Restrictions in Regression Analysis
cites work: Inequality Constrained Least-Squares Estimation
cites work: Optimal estimation theory for dynamic systems with set membership uncertainty: An overview
cites work: Identification and application of bounded-parameter models
cites work: Q4042591
cites work: Q3736716
full work available at URL: s0005-1098(02)00029-8
OpenAlex ID: W2084232218
description: scientific article; zbMATH DE number 1797090