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Classes of Influential Observations

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Classification and Knowledge Organization

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Summary

“This observation does not fit in with the trend”, “If the sample had been slightly different, we had reached another resul” — such statements shall be formalized. Regression diagnostics look for observations badly explained by the model. This is done by means of an influence measure that arises from a definition of influence. As influence has many aspects, there are many such diagnostics. Considering classical and Bayesian regression models, this work shall illustrate how to derive and use the diagnostics needed in specific cases.

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References

  • BELSLEY, D. A.; KUH, E. and WELSCH, R. E. (1980): Regression Diagnostics: Identifying Influential Data and Sources of Collinearity. Wiley, New York.

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  • CHATTERJEE, S. and HADI, A. S. (1988): Sensitivity Analysis in Linear Regression. Wiley, New York.

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  • COOK, R. D. and WEISBERG, S. (1982): Residuals and Influence in Regression. Chapman and Hall, New York.

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  • POLASEK, W. (1993): Variance Diagnostics for Classical and Bayesian Linear Regression. WWZ Discussion Papers Nr. 9302. WWZ, Universität Basel, Basel.

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  • WEISS, R. (1996): Sufficiency and Influence. Journal of the Royal Statistical Society Series B, to appear.

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Author information

Authors and Affiliations

  1. ISO/WWZ/Uni-Basel, Holbeinstr. 12, CH 4051, Basel, Switzerland

    R. Vonthein

Authors
  1. R. Vonthein
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Editors and Affiliations

  1. Institut für Medizinische Biometrie und Medizinische Informatik, University of Freiburg, Stefan-Meier-Straße 26, D-79104, Freiburg, Germany

    Rüdiger Klar

  2. University of Augsburg, D-86135, Augsburg, Germany

    Otto Opitz (Lehrstuhl für Mathematische Methoden der Wirtschaftswissenschaften) (Lehrstuhl für Mathematische Methoden der Wirtschaftswissenschaften)

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© 1997 Springer-Verlag Berlin Heidelberg

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Vonthein, R. (1997). Classes of Influential Observations. In: Klar, R., Opitz, O. (eds) Classification and Knowledge Organization. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-59051-1_24

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  • DOI: https://doi.org/10.1007/978-3-642-59051-1_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-62981-8

  • Online ISBN: 978-3-642-59051-1

  • eBook Packages: Springer Book Archive

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