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Designs for generalized linear models with several variables and model uncertainty

By D. C. Woods, S. M. Lewis, J. A. Eccleston and K. G. Russell

Abstract

Standard factorial designs may sometimes be inadequate for<br/>experiments that aim to estimate a generalized linear model, for<br/>example, for describing a binary response in terms of several<br/>variables. A method is proposed for finding exact designs for such<br/>experiments which uses a criterion that allows for uncertainty in<br/>the link function, the linear predictor or the model parameters,<br/>together with a design search. Designs are assessed and compared<br/>by simulation of the distribution of efficiencies relative to<br/>locally optimal designs over a space of possible models. Exact<br/>designs are investigated for two applications and their advantages<br/>over factorial and central composite designs are demonstrated

Topics: QA, HA
Year: 2006
OAI identifier: oai:eprints.soton.ac.uk:15828
Provided by: e-Prints Soton

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Citations

  1. (1980). A Comparison of Algorithms for Constructing Exact D-optimal designs,”
  2. (1994). A Simple Bayesian Modification of D-optimal Designs to Reduce Dependence on an Assumed Model,”
  3. (2000). Approximating Integrals via Monte Carlo and Deterministic Methods,
  4. (2004). Bayesian Experimental Design for Nonlinear Mixed-Effects Models with Application to
  5. (1995). Bayesian Experimental Design:
  6. (2006). Continuous Optimal Designs under Model Uncertainty,”
  7. (2006). Core Team
  8. (1996). Designs for Nonlinear and Generalized Linear Models,”
  9. (1985). Efficient Sequential Designs with Binary Data,”
  10. (1974). Experimental Design in a
  11. (1989). Generalized Linear Models,
  12. (2000). Minimax D-optimal Designs for the Logistic Model,”
  13. (1997). Model-Oriented Design of Experiments,
  14. (2001). On Optimal Designs for High Dimensional Binary Regression Models,”
  15. (1981). On the Existence of Maximum Likelihood Estimators for the Binomial Response Model,”
  16. (1989). Optimal Bayesian Design Applied to Logistic Regression Experiments,”
  17. (1987). Optimal Designs for Binary Data,”
  18. (1995). Optimal Designs for Binary Response Experiments with Two Variables,”
  19. (1995). Optimal Designs for Polynomial Regression when the Degree is not
  20. (1993). Optimal Experimental Design for Another’s Analysis,”
  21. (1971). Optimal Experimental Design for Polynomial Regression,”
  22. (1976). Optimal Multipurpose Designs for Regression Models,”
  23. (1997). Parameter Neutral Optimum Design for Non-Linear Models,”
  24. (1974). Planning Experiments for Discriminating Between Models (with discussion),”
  25. (1992). Robust Designs for
  26. (1982). Some Robust-Type D-optimal Designs in Polynomial Regession,”
  27. (1987). The Application of the Annealing Algorithm to the Construction of Exact Optimal Designs for Linear-Regression Models,”
  28. (1975). The Design of Experiments for Discriminating Between Two Rival Models,”
  29. (1996). The Existence of Maximum Likelihood Experiments from Designed Experiments,”
  30. (2000). The Theory of the Design of Experiments, Boca Raton: Chapman and Hall/CRC.
  31. (1992). The Use of a Canonical Form in the Construction of Locally Optimal Designs for Nonlinear Problems,”
  32. (1972). Theory of Optimal Experiments,

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