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Robust estimation in interlaboratory measurements with small number of measurements

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper two robust methods of assessing the value and the uncertainty of the measurand from the samples of small number of experimental data are presented and compared. Those methods can be used when some measurements results contain outliers, i.e. when the values of certain measurement results significantly differ from the others. They allow to set a credible statistical parameters of the measurements with the use of all experimental data. The following considerations are illustrated by the numerical examples of multi-laboratory measurement data key comparison. Compared are the results obtained by a classical method with rejection of outliers with two robust methods: a rescaled median absolute deviation MADS and an iterative two-criteria method. The paper also presents the advantages of the robust iterative statistical method in estimating the accuracy of the tested laboratory measurement results during its accreditation on the sample of four elements with outlier. A comparison with the estimates obtained by the standard procedure for evaluating performance accuracy is also provided.
Wydawca
Rocznik
Strony
104--110
Opis fizyczny
Bibliogr. 18 poz., rys., tab., wykr., wzory
Twórcy
  • National Technical University of Ukraine "KPI", Chair of Automation of Experimental Studies, Kiev, Ukraine
autor
  • Industrial Research Institute of Automation and Measurement (PIAP), Warszawa, Poland
Bibliografia
  • [1] Guide to the Expression of Uncertainty in Measurement (GUM), BIPM_JCGM 100:2008.
  • [2] ISO 5725-2:1994 - Accuracy (trueness and precision) of measurement methods and results. Part 2: Basic method for the determination of repeatability and reproducibility of a standard measurement method.
  • [3] EN ISO/IEC 17025: 2005 General requirements for the competence of testing and calibration laboratories. ICS 03.120.20.
  • [4] EN ISO/IEC 17043 Conformity assessment - General requirements for proficiency testing First ed. 2010-02-01.
  • [5] Belli M., Ellison S. L. et all: Implementation of proficiency testing schemes for a limited number of participants. Accreditation and Quality Assurance (2007) 12:391-398.
  • [6] Briggs P.: Proficiency testing for calibration laboratories. Proc. of XX IMEKO World Congress: Metrology for Green Growth.
  • [7] Tukey J. W.: Exploratory Data Analysis. Addison-Wesley. 1978.
  • [8] Olive David J.: Applied Robust Statistics. Southern Illinois University Department of Mathematics. June 23, 2008.
  • [9] Daszykowski M. M., Kaczmarek K., Van der Heyden Y.: Robust statistics in data analysis. A review basic concepts. Chemometrics and Intelligent Laboratory Systems 85 (2007) 203–219.
  • [10] Randa J.: Update to Proposal for KCRV & Degree of Equivalence for GTRF Key Comparisons. NIST, 2005 GT-RF / 2005-04 Internet.
  • [11] Huber P. J., Ronchetti E. M.: Robust Statistics. 2nd edition. Wiley, 2011 pp. 380.
  • [12] Volodarski E., Warsza Z., Koszewa L.: Robust evaluation of the accuracy of measurement methods. Pomiary Automatyka Kontrola (Measurements Automation Monitoring) nr 4 2012 s. 396-401 (in Polish),
  • [13] Wilrich P. T.: Robust estimates of the theoretical standard deviation to be used in interlaboratory precision experiments. Accreditation and Quality Assurance, May 2007, Volume 12, Issue 5, pp. 231-240.
  • [14] EURACHEM/CITAC. Guide CG 4 Quantifying Uncertainty in Analytical Measurement Third Edition QUAM:2012.P1.
  • [15] Volodarski E., Warsza Z., Koszewa L., Palianychko D.: Application of robust estimation in proficiency testing of laboratory by low number of measurements. Pomiary Automatyka Kontrola (Measurements Automation Monitoring) nr 6, 2012, (in Polish).
  • [16] Briggs P.: Proficiency testing for calibration laboratories. Proc. of XX IMEKO World Congress 2012: Metrology for Green Growth, Paper 286_F_O_9_36.
  • [17] Volodarsky Е. T., Warsza Z. L.: Examples of robust estimation with small number of measurements. Progress in Automation, Robotics and Measuring Techniques. (Editors: R. Szewczyk, C. Zieliński, M. Kaliczyńska), vol. 3 "Measuring Techniques and Systems". (ISBN 978-3-319-15834-1), vol. 352 of series: "Advances in Intelligent Systems and Computing" (ISSN2194-5357) Springer (2015) pp. 285 -291.
  • [18] Volodarsky Е. T., Warsza Z. L.: Application of two robust methods on the example of inter-laboratory comparison. In: Pavese, F., Bremser, W., Chunovkina, A. G., Fischer, N., Forbes, A. B. (eds.) Advanced Mathematical and Computational Tools in Metrology and Testing, X. Series on Advances in Mathematics for Applied Sciences volume 86, World Scientific Publishing Company, pp. 385-391, 2015.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-7b74142b-f075-40a1-a88d-c46fa1103416
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