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L1-Norm and L∞-Norm Estimation: An Introduction to the Least Absolute Residuals, the Minimax Absolute Residual and Related Fitting Procedures: SpringerBriefs in Statistics

Autor Richard Farebrother
en Limba Engleză Paperback – 16 apr 2013
This monograph is concerned with the fitting of linear relationships in the context of the linear statistical model. As alternatives to the familiar least squared residuals procedure, it investigates the relationships between the least absolute residuals, the minimax absolute residual and the least median of squared residuals procedures. It is intended for graduate students and research workers in statistics with some command of matrix analysis and linear programming techniques.​
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Specificații

ISBN-13: 9783642362996
ISBN-10: 3642362990
Pagini: 60
Ilustrații: VI, 58 p.
Dimensiuni: 155 x 235 x 15 mm
Greutate: 0.1 kg
Ediția:2013
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seria SpringerBriefs in Statistics

Locul publicării:Berlin, Heidelberg, Germany

Public țintă

Graduate

Cuprins

Introduction.- Point Fitting Problems in One- and Two-dimensions.- The Hyperplane Fitting Problem in Two or More Dimensions.- Linear Programming Computations.- Statistical Theory.- The Least Median of Squared Residuals Procedure.- Mechanical Representations.- References.- Index of Names. ​

Notă biografică

Dr. Richard William Farebrother was a member of the teaching staff of the Victoria University of Manchester 1970-1993 and an Honorary Reader in Econometrics 1993-2001. He has published three books: Linear Least Squares Computations (1988), Fitting Linear Relationships (1999), and Visualizing Statistical Models and Concepts (2002). He has also published more than 150 research papers.

Textul de pe ultima copertă

This monograph is concerned with the fitting of linear relationships in the context of the linear statistical model. As alternatives to the familiar least squared residuals procedure, it investigates the relationships between the least absolute residuals, the minimax absolute residual and the least median of squared residuals procedures. It is intended for graduate students and research workers in statistics with some command of matrix analysis and linear programming techniques.​

Caracteristici

Single source of information about this important area of research Wide-ranging discussion of least absolute residuals, minimax residual and least median of squared residuals fitting procedures Several new results not previously published in book form Includes supplementary material: sn.pub/extras