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Simultaneous inference for model averaging of derived parameters.

Signe M Jensen1, Christian Ritz

  • 1Department of Nutrition, Exercise and Sports, University of Copenhagen, Nrregade 10, 1165, Kbenhavn, Denmark.

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Summary
This summary is machine-generated.

Model averaging helps quantify uncertainty, but current methods struggle with simultaneous inference for derived parameters. This study introduces a new approach for accurate standard errors and confidence intervals in model-averaged derived parameters.

Keywords:
Asymptotic representationWald-type intervalsbenchmark dosecoveragedose response

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Area of Science:

  • Statistics
  • Econometrics
  • Biostatistics

Background:

  • Model averaging is crucial for addressing model selection uncertainty.
  • Existing methods for quantifying this uncertainty often fail to extend to simultaneous inference.
  • There is a practical need for model averaging alongside simultaneous inference for derived parameters.

Purpose of the Study:

  • To propose a novel method for obtaining asymptotically correct standard errors for model-averaged derived parameters.
  • To develop a technique for constructing simultaneous confidence intervals that control the family-wise Type I error rate.
  • To address limitations in current model averaging techniques regarding simultaneous inference.

Main Methods:

  • Development of a new statistical methodology for standard error estimation in model averaging.
  • Application of the method to derive simultaneous confidence intervals for model-averaged parameters.
  • Evaluation of the method's performance using simulation studies and real-world examples.

Main Results:

  • The proposed method yields asymptotically correct standard errors for model-averaged derived parameters.
  • Simultaneous confidence intervals generated by the method asymptotically control the family-wise Type I error rate.
  • Simulation studies confirm the method's coverage performance.

Conclusions:

  • The new method effectively handles uncertainty in model selection for derived parameters.
  • It provides a robust framework for simultaneous inference in model-averaged settings.
  • The approach is applicable across various scientific disciplines requiring complex statistical modeling.