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Optimal experimental design for generalized linear models is often done using a pseudo-Bayesian approach that integrates the design criterion across a prior distribution on the parameter values. This approach

Optimal experimental design for generalized linear models is often done using a pseudo-Bayesian approach that integrates the design criterion across a prior distribution on the parameter values. This approach ignores the lack of utility of certain models contained in the prior, and a case is demonstrated where the heavy focus on such hopeless models results in a design with poor performance and with wild swings in coverage probabilities for Wald-type confidence intervals.

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    Date Created
    • 2015
    Resource Type
  • Text
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    Note
    • Partial requirement for: Ph.D., Arizona State University, 2015
      Note type
      thesis
    • Includes bibliographical references (pages 73-77)
      Note type
      bibliography
    • Field of study: Industrial engineering

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    by Edgar Hassler

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