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L1-Norm and L∞-Norm Estimation [electronic resource] : An Introduction to the Least Absolute Residuals, the Minimax Absolute Residual and Related Fitting Procedures / by Richard William Farebrother.

By: Contributor(s): Material type: TextTextSeries: SpringerBriefs in StatisticsPublisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013Description: VI, 58 p. online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9783642363009
Subject(s): Additional physical formats: Printed edition:: No titleDDC classification:
  • 519.5 23
LOC classification:
  • QA276-280
Online resources:
Contents:
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.  .
In: Springer eBooksSummary: 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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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.  .

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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