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Summary

This paper introduces the Bayesian Simulation Study Framework (BASIS) to improve the reporting of Bayesian simulation studies. BASIS offers a structured approach for planning, executing, and analyzing these crucial statistical research components.

Keywords:
Bayesian simulation study (BASIS) frameworkBayesian statisticsmethodological researchreproducibility of researchstatistical simulation studies

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Area of Science:

  • Computational Statistics
  • Biometrical Research

Background:

  • Statistical simulation studies are vital for evaluating computational methods.
  • Existing literature reveals significant reporting deficiencies in simulation studies, particularly Bayesian ones.

Purpose of the Study:

  • To present the Bayesian Simulation Study Framework (BASIS) for enhanced reporting of Bayesian simulation studies.
  • To provide a structured approach for the entire lifecycle of Bayesian simulation studies.

Main Methods:

  • Development of the BASIS framework, integrating previous recommendations.
  • Inclusion of computational aspects like MCMC convergence diagnostics and sensitivity analyses.
  • Incorporation of current guidelines for Bayesian analyses.

Main Results:

  • BASIS offers a structured skeleton for planning, coding, execution, analysis, and reporting.
  • The framework addresses computational elements essential for robust Bayesian simulations.
  • It promotes neutral comparison studies in statistical research.

Conclusions:

  • The BASIS framework significantly improves the structure and reporting of Bayesian simulation studies.
  • It serves as a valuable guide for both methodological researchers and users of simulation results.
  • BASIS enhances the reliability and comparability of computational statistics research.