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Metrika

, Volume 42, Issue 1, pp 29–48 | Cite as

Optimal and robust invariant designs for cubic multiple regression

  • Norbert Gaffke
  • Berthold Heiligers
Article

Keywords

Stochastic Process Probability Theory Economic Theory Invariant 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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References

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  3. Gaffke N, Heiligers B (1994) Computing optimal approximate invariant designs for cubic regression on multidimensional balls and cubes. Report No 530. Forschungsschwerpunbt der DFG “Anwendungsbezogene Optimierung und Steuerung”, Universität AugsburgGoogle Scholar
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  7. Galil Z, Kiefer J (1979) Extrapolation designs and φp-optimum designs for cubic regression on theq-ball. J Statist Planning Inference 3:27–38Google Scholar
  8. Heiligers B (1992) Admissible experimental designs in multiple polynomial regression. J Statist Planning Inference 31:219–233Google Scholar
  9. Heiligers B, Schneider K (1992) Invariant admissible and optimal designs in cubic regression on the ν-ball. J Statist Planning Inference 31:113–125Google Scholar
  10. Kiefer J (1974) General equivalence theory for optimum designs (approximate theory). Ann Math Statist 75:849–879Google Scholar
  11. Pesotchinsky L (1978) φp-optimal second order designs for symmetric regions. J Statist Planning Inference 2:173–188Google Scholar
  12. Pukelsheim F (1993) Optimal design of experiments. Wiley, New YorkGoogle Scholar
  13. Rockafellar RT (1970) Convex analysis. Princeton, New JerseyGoogle Scholar

Copyright information

© Physica-Verlag 1995

Authors and Affiliations

  • Norbert Gaffke
    • 1
  • Berthold Heiligers
    • 1
  1. 1.Institut für MathematikUniversität AugsburgAugsburgGermany

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