MODELLING EXPERT KNOWLEDGE TO ASSIGN CONSENSUS VALUES IN PROFICIENCY TESTS

摘要

In this paper auxiliary information on laboratories is combined with proficiency testing (PT) data to compute more reliable consensus values and associated uncertainties. This new methodology extensively relies on expert knowledge to assume measurement bias, to investigate the sources of measurement bias, to model relationships between sources of bias and to make hypotheses on the form of the consensus value computed as a weighted mean of the measurement results. This approach thus extends the modelling of PT data to the modelling of measurement bias. As such, the approach provides additional results aimed at diagnosing major sources of measurement bias. The full methodology is applied to a PT involving environmental laboratories measuring water pollutants.