Analysis of key comparisons: Estimating laboratories biases by a fixed effects model using Bayesian Model Averaging
Blaza Toman, Clemens Elster
We propose a novel procedure for the analysis of key comparison data. The goal of the procedure is to detect biases in the reported measurement results which are not accounted for by quoted uncertainties. A fixed effects bias model is employed which constrains the biases of some of the laboratories to zero. Only the number of these laboratories needs to be specified, not the laboratories themselves. The analysis then runs through all possible different models, each assuming zero biases for a different subset of laboratories. The results from these models are finally merged by employing a Bayesian Model Averaging technique. Explicit formulae are derived which allow for an easy application of the proposed approach. The procedure is illustrated by its application to data from the CCL-K1 key comparison.