Simultaneous Inference for Model Averaging of Derived Parameters

  • Jensen S
  • Ritz C
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Abstract

Model averaging is a useful approach for capturing uncertainty due to model selection. Currently, this uncertainty is often quantified by means of approxiJensen, S. M., & Ritz, C. (2014). Simultaneous Inference for Model Averaging of Derived Parameters. Risk Analysis : An Official Publication of the Society for Risk Analysis. doi:10.1111/risa.12242mations that do not easily extend to simultaneous inference. Moreover, in practice there is a need for both model averaging and simultaneous inference for derived parameters calculated in an after-fitting step. We propose a method for obtaining asymptotically correct standard errors for one or several model-averaged estimates of derived parameters and for obtaining simultaneous confidence intervals that asymptotically control the family-wise Type I error rate. The performance of the method in terms of coverage is evaluated using a simulation study and the applicability of the method is demonstrated by means of three concrete examples.

Author-supplied keywords

  • Asymptotic representation
  • Benchmark dose
  • Coverage
  • Dose response
  • Wald-type intervals

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Authors

  • Signe M. Jensen

  • Christian Ritz

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