Evaluation of uncertainty in the adjustment of fundamental constants

TLDR: This work investigates the reliability and robustness of the location-scale model and the random effects model with particular focus on resulting coverage or credible intervals and suggests that the proposed Bayesian inference based on therandom effects model is more reliable and less sensitive to model misspecifications than the approach based onThe location- Scale model.

Abstract: Combining multiple measurement results for the same quantity is an important task in metrology and in many other areas. Examples include the determination of fundamental constants, the calculation of reference values in interlaboratory comparisons, or the meta-analysis of clinical studies. However, neither the GUM nor its supplements give any guidance for this task. Various approaches are applied such as weighted least-squares in conjunction with the Birge ratio or random effects models. While the former approach, which is based on a location-scale model, is particularly popular in metrology, the latter represents a standard tool used in statistics for meta-analysis. We investigate the reliability and robustness of the location-scale model and the random effects model with particular focus on resulting coverage or credible intervals. The interval estimates are obtained by adopting a Bayesian point of view in conjunction with a non-informative prior that is determined by a currently favored principle for selecting non-informative priors. Both approaches are compared by applying them to simulated data as well as to data for the Planck constant and the Newtonian constant of gravitation. Our results suggest that the proposed Bayesian inference based on the random effects model is more reliable and less sensitive to model misspecifications than the approach based on the location-scale model.