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L1-norm and L[infinity symbol]-norm estimation : an introduction to the least absolute residuals, the minimax absolute residual and related fitting procedures Titelvorschau
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L1-norm and L[infinity symbol]-norm estimation : an introduction to the least absolute residuals, the minimax absolute residual and related fitting procedures

Verfasser/in: R W Farebrother
Verlag: Berlin ; New York : Springer, ©2013.
Serien: SpringerBriefs in statistics.
Ausgabe/Format   E-Book : Dokument : EnglischAlle Ausgaben und Formate anzeigen
Datenbank:WorldCat
Zusammenfassung:
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  Weiterlesen…
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Gattung/Form: Electronic books
Medientyp: Dokument, Internetquelle
Dokumenttyp: Internet-Ressource, Computer-Datei
Alle Autoren: R W Farebrother
ISBN: 9783642363009 3642363008 3642362990 9783642362996
OCLC-Nummer: 839442346
Beschreibung: 1 online resource (vi, 58 pages).
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.
Serientitel: SpringerBriefs in statistics.
Verfasserangabe: Richard William Farebrother.
Weitere Informationen:

Abstract:

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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