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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 / by Richard William Farebrother.

Por: Colaborador(es): Tipo de material: TextoTextoSeries SpringerBriefs in StatisticsEditor: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013Descripción: vI, 58 páginas recurso en líneaTipo de contenido:
  • texto
Tipo de medio:
  • computadora
Tipo de portador:
  • recurso en línea
ISBN:
  • 9783642363009
Formatos físicos adicionales: Edición impresa:: Sin títuloClasificación LoC:
  • QA276-280
Recursos en línea:
Contenidos:
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.  .
Resumen: 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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Springer eBooks

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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