Algebraic relationships between classical regression and total least- squares estimation
type: Item
instance of: scholarly article
title: Algebraic relationships between classical regression and total least- squares estimation
zbMATH Open document ID: 0631.65150
DOI: 10.1016/S0024-3795(87)90320-X
author: Sabine Van Huffel
author: Joos Vandewalle
published in: Linear Algebra and its Applications
publication date: 1987-01-01T00:00:00Z
review text: The classical linear regression model \(y=X\beta +\epsilon\) can be extended to a linear errors-in-variables model which considers all observations X, y subject to measurement errors. For these problems, the total least-squares (TLS) approach yields strongly consistent estimators. The authors derive some algebraic equivalences and relationships between classical estimators and TLS in the presence of collinearities.
Mathematics Subject Classification ID: 65C99
Mathematics Subject Classification ID: 65F20
Mathematics Subject Classification ID: 62J05
zbMATH DE Number: 4026786
zbMATH Keywords: linear regression
zbMATH Keywords: linear errors-in-variables model
zbMATH Keywords: total least-squares
zbMATH Keywords: strongly consistent estimators
MaRDI profile type: Publication
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description: scientific article; zbMATH DE number 4026786