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Statistics and Computing

, Volume 27, Issue 3, pp 845–863 | Cite as

Exact optimal experimental designs with constraints

  • Mercedes Esteban-Bravo
  • Agata Leszkiewicz
  • Jose M. Vidal-SanzEmail author
Article

Abstract

The experimental design literature has produced a wide range of algorithms optimizing estimator variance for linear models where the design-space is finite or a convex polytope. But these methods have problems handling nonlinear constraints or constraints over multiple treatments. This paper presents Newton-type algorithms to compute exact optimal designs in models with continuous and/or discrete regressors, where the set of feasible treatments is defined by nonlinear constraints. We carry out numerical comparisons with other state-of-art methods to show the performance of this approach.

Keywords

Exact optimal experimental designs constrained designs Newton-type algorithms 

Notes

Acknowledgments

Research partly supported by MICINN (Spain) Grant ECO2011-30198 and ECO2015-67763-R. The authors would like to thank the Editor and two anonymous reviewers for their valuable comments. The names of the authors appear in alphabetical order.

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Mercedes Esteban-Bravo
    • 1
  • Agata Leszkiewicz
    • 2
  • Jose M. Vidal-Sanz
    • 1
    Email author
  1. 1.Department of Business AdministrationUniversidad Carlos III de MadridGetafeSpain
  2. 2.Center for Excellence in Brand and Customer ManagementRobinson College of Business, Georgia State UniversityAtlantaUSA

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