Open Access
Int. J. Metrol. Qual. Eng.
Volume 1, Number 1, 2010
Page(s) 7 - 9
Published online 19 April 2010
  1. F. Pavese, An introduction to data modeling principles in metrology and testing, in Data Modeling for Metrology and Testing in Measurement Science. Series: Modeling and Simulation in Science, Engineering and Technology, edited by F. Pavese, A.B. Forbes (Birkhäuser, Boston, 2009), Chap. 1, pp. 1–30 [Google Scholar]
  2. F. Pavese, A metrologist viewpoint on some statistical issues concerning the comparison of non-repeated measurement data, namely MRA key comparisons, Measurement 39, 821 (2006) [Google Scholar]
  3. R.N. Kacker, R.U. Datla, A.C. Parr, Statistical analysis of CIPM key comparisons based on the ISO, Metrologia 41, 340 (2004) [Google Scholar]
  4. F. Pavese, The Definition of the Measurand in Key Comparisons: lessons learnt with thermal standards, Metrologia 44, 327 (2007) [Google Scholar]
  5. F. Pavese, Metrologia 42, L10 (2005) [Google Scholar]
  6. P. Ciarlini, M.G. Cox, F. Pavese, G. Regoliosi, The use of a mixture of probability distributions in temperature interlaboratory comparisons, Metrologia 41, 116 (2004) [Google Scholar]
  7. D.L. Duewer, A comparison of location estimators for interlaboratory data contaminated with value and uncertainty outliers, Accred. Qual. Assur. 13, 193 (2008) [Google Scholar]
  8. R.J. Douglas, A.G. Steele, Pair-differences chi-squared statistics for Key Comparisons, Metrologia 43, 89 (2006) [Google Scholar]
  9. A.G. Steele, R.J. Douglas, Establishing confidence from measurement comparisons, Measur. Sci. Technol. 19, 064003 (2008), doi: 10.1088/0957-0233/19/6/064003 [Google Scholar]

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