By Franco (EDT)/ Forbes, Alistair B. (EDT) Pavese
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Additional info for Advances in Data Modeling for Measurements in the Metrology and Testing Fields
Using eﬀective degrees of freedom Metrologia 40 31–35 Chunovkina A and Cox M G 2003 A model–based approach to key comparison data evaluation, in Proceedings 17th IMEKO World Congress, Cavtat, Slovenia, on CD–ROM Chunovkina A, Ciarlini P, Cox M, and Pavese F 2004 Handling and treatment of measurement data from diﬀerent groups or sources, in Proceedings 10th IMEKO TC7 International Symposium, on CD–ROM Ciarlini P, Cox M G, Pavese F, and Regoliosi G 2004 The use of a mixture of probability distributions in temperature interlaboratory comparisons Metrologia 41 116–121 Ciarlini P, Chunovkina A, Pavese F, and Regoliosi G 2005 Mixture distribution models in temperature metrology and the assessment of the degree of equivalence, in Proceedings TEMPMEKO 2004, LPM, Zabreb, Croatia, pp 1003–1008 Cox M G, Forbes A B, and Harris P 2007 Distributions associated with measurands, in PTB–BIPM Workshop on the Impact of Information Technology in Metrology, Berlin, Germany.
Approach B. A test is performed to check for consistency of the data with the assumption of repeated measurements. , see [SD06] and references therein). Test failure shall not pass the hypothesis that the measurements are repeated. Test acceptance shall not change the fact that they intrinsically are non-repeated measurement. Output summaries of the (key-)comparison 32 . It is vital to compute correctly the summary statistics and, in the case of a MRA key comparison (KC) [MRA99, CIPM99], the speciﬁc output parameters summarising the results obtained: – The key comparison reference value (KCRV, when necessary [Pav07]) – The degrees of equivalence (DoE, both bilateral and with respect to the KCRV) – Any possible ‘signiﬁcant unresolved deviation’ (SUD) – The links between diﬀerent KCs, for their repository in the BIPM database [KCDB] and their use for the ‘calibration measurement capabilities’ (CMC).
Then the mixture density N πi fi (x; Λ(i) ), g(x; Λ) = (7) i=1 where (Λ,π) are the mixture parameters, g is assumed to characterise the total data variability related to the output pdf. In model (7), Λ = (Λ(1) , . . , Λ(N ) ) contains the parameters of the N participants’ pdfs and the π i are positive proportions summing to unity. It may often be appropriate to take these 35 Actually in most cases only of the (yh – yr ), where yr is the value attributed to a reference standard, generally a transfer standard.