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Part of the book series: Lecture Notes in Statistics ((LNS,volume 113))

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Abstract

In general, the set of all competing designs is rather large. Therefore, it is necessary to use tools which simplify the characterization of optimum designs and which permit to search for an optimum design in a substantially smaller subclass. The first part of this section is devoted to tools which deal with reductions to subsystems of regression functions and, hence, to subsystems of parameters. In the second part a further inherent structure is assumed in the underlying model which allows for reduction by invariance with respect to suitable transformations on the design region.

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© 1996 Springer-Verlag New York, Inc.

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Schwabe, R. (1996). Reduction Principles. In: Optimum Designs for Multi-Factor Models. Lecture Notes in Statistics, vol 113. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-4038-9_3

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  • DOI: https://doi.org/10.1007/978-1-4612-4038-9_3

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-0-387-94745-7

  • Online ISBN: 978-1-4612-4038-9

  • eBook Packages: Springer Book Archive

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