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Optimum Designs for Multi-Factor Models

  • Rainer¬†Schwabe

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

Table of contents

  1. Front Matter
    Pages i-vii
  2. General Concepts

    1. Front Matter
      Pages 1-1
    2. Rainer Schwabe
      Pages 2-7
    3. Rainer Schwabe
      Pages 8-14
    4. Rainer Schwabe
      Pages 15-33
  3. Particular Classes of Multi-factor Models

    1. Front Matter
      Pages 35-37
    2. Rainer Schwabe
      Pages 38-48
    3. Rainer Schwabe
      Pages 49-75
    4. Rainer Schwabe
      Pages 76-106
    5. Rainer Schwabe
      Pages 107-111
  4. Back Matter
    Pages 112-126

About this book

Introduction

In real applications most experimental situations are influenced by a large number of different factors. In these settings the design of an experiment leads to challenging optimization problems, even if the underlying relationship can be described by a linear model. Based on recent research, this book introduces the theory of optimum designs for complex models and develops general methods of reduction to marginal problems for large classes of models with relevant interaction structures.

Keywords

Factor Partition Variance addition design interaction matrices model optimization review

Authors and affiliations

  • Rainer¬†Schwabe
    • 1
  1. 1.Fachbereich Mathematik und InformatikFreie Universitat BerlinBerlinGermany

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4612-4038-9
  • Copyright Information Springer-Verlag New York 1996
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Print ISBN 978-0-387-94745-7
  • Online ISBN 978-1-4612-4038-9
  • Series Print ISSN 0930-0325
  • Buy this book on publisher's site