Download PDF by Franco (EDT)/ Forbes, Alistair B. (EDT) Pavese: Advances in Data Modeling for Measurements in the Metrology

By Franco (EDT)/ Forbes, Alistair B. (EDT) Pavese

ISBN-10: 0817645926

ISBN-13: 9780817645922

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Extra info for Advances in Data Modeling for Measurements in the Metrology and Testing Fields

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2 Thus, the expectation μ = a + ε + ε and Ei + E ∼ N(0, σi2 + σ ), with μ 2 and σ being unknown parameters. Nonprobabilistic systematic errors Opposite to the previous approach and to GUM, in [Gra01,Gra05] the systematic errors are preferred to be treated as nonprobabilistic, “to make allowance for them by introducing biases and worst–case estimations”. The plurality of “unknown systematic errors” are supposed to bring to an overall effect, a constant-in-time unknown systematic error f , whose value is supposed to fall into a confining interval f1 ≤ f ≤ f2 .

An Introduction to Data Modelling Principles in Metrology and Testing 17 most relevant cases, with some exceptions summarised below in “Other Approaches”. Approach A The prior knowledge is used, consisting of the evidence that for comparisons in general, “when the i–th participant repeats the comparison j times, then its results can be distributed about an expectation value differing from the measurand value a by an amount bi with standard deviation si ” [Whi00], where bi has the same meaning as in model (2), and is normally called the ‘laboratory component of bias’ (with risk of confusion with the meaning of this word in testing).

In fact, if the degree of consistency could determine how the KCRV is calculated, it will therefore influence the KCRV and the DoEs. However, MRA prescribes that the reported results cannot be adjusted because the DoEs are to be defined in terms of the (unadjusted) data. Mixture model A factor influencing the estimate of compatibility or consistency of the input comparison data is the nature of the standards being compared (cfr. , [Eve81, McLP00]). The probabilistic model, called the mixture model, can provide a characterisation of the overall stochastic variability of the population of a comparison involving Class 2 standards, that is, when not involving artefacts (Class 1) [CCPR04].

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Advances in Data Modeling for Measurements in the Metrology and Testing Fields by Franco (EDT)/ Forbes, Alistair B. (EDT) Pavese


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