L1-Norm and L -Norm Estimation

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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.


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

Autorentext
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.

Inhalt
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.

Weitere Informationen

  • Allgemeine Informationen
    • GTIN 09783642362996
    • Sprache Englisch
    • Auflage 2013
    • Größe H235mm x B155mm x T4mm
    • Jahr 2013
    • EAN 9783642362996
    • Format Kartonierter Einband
    • ISBN 3642362990
    • Veröffentlichung 16.04.2013
    • Titel L1-Norm and L -Norm Estimation
    • Autor Richard Farebrother
    • Untertitel An Introduction to the Least Absolute Residuals, the Minimax Absolute Residual and Related Fitting Procedures
    • Gewicht 113g
    • Herausgeber Springer Berlin Heidelberg
    • Anzahl Seiten 64
    • Lesemotiv Verstehen
    • Genre Mathematik

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