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Optimal Designs for Contaminated Linear Regression

  • Norbert Gaffke
Part of the Theory and Decision Library book series (TDLB, volume 1)

Abstract

One notion of robustness of linear regression designs refers to moderate deviations of the regression function from the ideal linear regression setup, which may be modelled by including additive contamination functions. These may be caused for example in polynomial regression by higher order terms, which have not been included in the ideal model. The paper shows how the concepts of optimal design theory for linear regression can be extended to contaminated linear regression and points out the main problems arising in the contaminated case.

Keywords

Regression Function Linear Estimator Borel Probability Measure Approximate Theory Optimal Robust Design 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Academy of Agricultural Sciences of the GDR, Research Centre of Animal Production, Dummerstorf-Rostock, DDR 2551 Dummerstorf. 1984

Authors and Affiliations

  • Norbert Gaffke
    • 1
  1. 1.Institut für Statistik und WirtschaftsmathematikRWTH AachenGermany

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