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Combining Soft Computing and Statistical Methods in Data Analysis pp 329–335Cite as

Elements of Robust Regression for Data with Absolute and Relative Information

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Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 77))

Abstract

Robust regression methods have advantages over classical least-squares (LS) regression if the strict model assumptions used for LS regression are violated. We briefly review LMS and LTS regression as robust alternatives to LS regression, and illustrate their advantages. Furthermore, it is demonstrated how robust regression can be used if the response variable contains relative rather than absolute information.

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References

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Author information

Authors and Affiliations

  1. Palacký University, 77146, Olomouc, Czech Republic

    Karel Hron

  2. Department of Statistics and Probability Theory, Vienna University of Technology, 1040, Vienna, Austria

    Peter Filzmoser

Authors
  1. Karel Hron
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  2. Peter Filzmoser
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Editor information

Editors and Affiliations

  1. Research Unit on Intelligent Data Analysis and Graphical Models, European Centre for Soft Computing Edificio Científico-Tecnológico. 3a Planta, C/ Gonzalo Gutiérrez Quirós s/n, 33600, Mieres, Spain

    Christian Borgelt , Gil González-Rodríguez  & Wolfgang Trutschnig ,  & 

  2. Departamento de Estadística e I.O. y D.M., Universidad de Oviedo, Facultad de Ciencias, C/ Calvo Sotelo s/n, 33007, Oviedo, Spain

    María Asunción Lubiano

  3. Departamento de Estadística e I.O. yD.M., Universidad de Oviedo, Facultad de Ciencias, C/ Calvo Sotelo s/n, 33007, Oviedo, Spain

    María Ángeles Gil

  4. Systems Research Institute, Polish Academy of Sciences, Newelska 6, 01-447, Warsaw, Poland

    Przemysław Grzegorzewski

  5. Systems Research Institute, Polish Academy of Science, Newelska 6, 01-447, Warsaw, Poland

    Olgierd Hryniewicz

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© 2010 Springer-Verlag Berlin Heidelberg

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Hron, K., Filzmoser, P. (2010). Elements of Robust Regression for Data with Absolute and Relative Information. In: Borgelt, C., et al. Combining Soft Computing and Statistical Methods in Data Analysis. Advances in Intelligent and Soft Computing, vol 77. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14746-3_41

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  • DOI: https://doi.org/10.1007/978-3-642-14746-3_41

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-14745-6

  • Online ISBN: 978-3-642-14746-3

  • eBook Packages: EngineeringEngineering (R0)

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