The Total Least Squares Problem: Computational Aspects and Analysis

The Total Least Squares Problem: Computational Aspects and Analysis

by Sabine van Huffel, Joos Vandewalle
     
 

ISBN-10: 0898712750

ISBN-13: 9780898712759

Pub. Date: 01/28/1991

Publisher: SIAM

This is the first book devoted entirely to total least squares. The authors give a unified presentation of the TLS problem. A description of its basic principles are given, the various algebraic, statistical and sensitivity properties of the problem are discussed, and generalizations are presented. Applications are surveyed to facilitate uses in an even wider range of

Overview

This is the first book devoted entirely to total least squares. The authors give a unified presentation of the TLS problem. A description of its basic principles are given, the various algebraic, statistical and sensitivity properties of the problem are discussed, and generalizations are presented. Applications are surveyed to facilitate uses in an even wider range of applications. Whenever possible, comparison is made with the well-known least squares methods. A basic knowledge of numerical linear algebra, matrix computations, and some notion of elementary statistics is required of the reader; however, some background material is included to make the book reasonably self-contained.

Product Details

ISBN-13:
9780898712759
Publisher:
SIAM
Publication date:
01/28/1991
Series:
Frontiers in Applied Mathematics Series
Edition description:
New Edition
Pages:
300
Product dimensions:
5.98(w) x 8.98(h) x 0.67(d)

Related Subjects

Table of Contents

Introduction; Basic principles of the total least squares problem; Extensions of the basic total least squares problem; Direct speed Improvement of the total least squares computations; Iterative speed Improvement for solving slowly varying total least squares problems; Algebraic Connections Between total least squares and least squares problems; Sensitivity analysis of total least squares and least squares problems in the presence of errors in all data; Statistical properties of the total least squares problem; Algebraic connections between total least squares estimation and classical linear regression in Multicollinearity problems; Conclusions.

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