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This special issue of
Metrologia is the first that is not devoted to units, or
constants, or measurement techniques in some specific field of
metrology, but to the generic topic of statistical and
probabilistic methods for metrology. The number of papers on this
subject in measurement journals, and in
Metrologia in particular, has continued to increase over the
years, driven by the publication of the
Guide to the Expression of Uncertainty in Measurement (GUM)
[1] and the Mutual Recognition Arrangement (MRA) of the CIPM [2].
The former stimulated metrologists to think in greater depth about
the appropriate modelling of their measurements, in order to
provide uncertainty evaluations associated with measurement
results. The latter obliged the metrological community to
investigate reliable measures for assessing the calibration and
measurement capabilities declared by the national metrology
institutes (NMIs).
Furthermore, statistical analysis of measurement data became
even more important than hitherto, with the need, on the one hand,
to treat the greater quantities of data provided by sophisticated
measurement systems, and, on the other, to deal appropriately with
relatively small sets of data that are difficult or expensive to
obtain.
The importance of supporting the GUM and extending its
provisions was recognized by the formation in the year 2000 of
Working Group 1, Measurement uncertainty, of the Joint Committee
for Guides in Metrology. The need to provide guidance on key
comparison data evaluation was recognized by the formation in the
year 2001 of the BIPM Director's Advisory Group on Uncertainty. A
further international initiative was the revision, in the year
2004, of the remit and title of a working group of ISO/TC 69,
Application of Statistical Methods, to reflect the need to
concentrate more on statistical methods to support measurement
uncertainty evaluation.
These international activities are supplemented by national
programmes such as the Software Support for Metrology programme in
the UK, which includes within its main themes generic items related
to modelling, uncertainty evaluation and key comparisons. There are
also teams concentrating on statistics within a metrology
environment, the largest of which is the Statistical Engineering
Division at NIST. There are, however, key pockets of mathematical
and statistical expertise at all major and many of the smaller
NMIs. Academia also makes considerable input to metrological
thinking. The papers in this special issue reflect the above
considerations—and more.
There are several offerings relating to the GUM: (a) the manner
in which the GUM is evolving, especially through Supplements to the
GUM, (b) a comparison of the GUM, the GUM Supplement concerned with
the propagation of distributions and Bayesian statistics, in the
context of linear calibration, (c) theoretical and practical
aspects of the use of a Monte Carlo method for propagating
distributions, (d) the use of a generalization of the sensitivity
coefficients in the GUM to correlated quantities, and (e)
considerations on obtaining best estimates when the model is
non-linear.
At a more fundamental level, a systematic and versatile approach
to developing the model of measurement, on which uncertainty
evaluation is of course based, is presented, and a paper is
included on principles of probability and statistics that promote
sound decision-making.
The evaluation of key comparison data is represented in terms of
contributions relating to (a) models of key comparisons, with
measures of operability and interoperability, (b) a Bayesian
procedure for providing PDFs from which the measures required by
the MRA can be extracted, (c) an extension of the
E
n measure familiar to many metrologists, and (d) the
use of the median and weighted median as the key comparison
reference value in the presence of discrepant measurement
results.
The remaining contributions concern the analysis of measurement
data, including spectral analysis. Covered are (a) a comparison of
conventional and Bayesian approaches to the evaluation of data
subject only to random effects, (b) element-wise weighted least
squares and its comparison with conventionally weighted and total
least squares, (c) the generalized weighted mean of correlated
quantities (with an application to key comparisons), (d) the
uncertainty associated with the average of autocorrelated
quantities, (e) the fitting of three-dimensional geometric elements
to coordinate data, (f) the fitting of calibration curves from data
having general uncertainty structure, (g) the estimation of the
power spectrum of clock noise, (h) Kalman filtering, also in the
presence of clock noise, and (i) an improved Allan deviation-like
statistic.
The realization of such a rich issue involved considerable
efforts from many contributors. Thanks are due to the former
Editor, Peter Martin, who first conceived the idea of this issue,
to Jeffrey Williams, the present Editor, for his support to its
realization, to the authors for their contributions, and to the
referees who gave their time to review and comment on the
papers.
References
[1] BIPM, IEC, IFCC, ISO, IUPAC, IUPAP and OIML 1995
Guide to the Expression of Uncertainty in Measurement
(Geneva, Switzerland: International Organisation for
Standardisation) ISBN 92-67-10188-9
[2] BIPM 1999
Mutual Recognition of National Measurement Standards and of
Calibration and Measurement Certificates Issued by National
Metrology Institutes (Sèvres, France: Bureau
International des Poids et Mesures)