À propos de cet article

Citez

[1] JCGM. (2008). Evaluation of measurement data — Guide to the expression of uncertainty in measurement. JCGM 100:2008 (GUM 1995 with minor corrections). Search in Google Scholar

[2] JCGM. (2008). Evaluation of measurement data — Supplement 1 to the Guide to the expression of uncertainty in measurement — Propagation of distributions using a Monte Carlo method. JCGM 101:2008. Search in Google Scholar

[3] JCGM. (2011). Evaluation of measurement data — Supplement 2 to the Guide to the expression of uncertainty in measurement — Extension to any number of output quantities. JCGM 102:2011. Search in Google Scholar

[4] JCGM. (2012). International vocabulary of metrology – Basic and general concepts and associated terms (VIM), 3rd Edition. JCGM 200:2012. Search in Google Scholar

[5] ISO. (2010). Determination and use of straight-line calibration functions. ISO/TS 28037:2010. Search in Google Scholar

[6] ISO. (2006). Statistics – Vocabulary and symbols – Part 1: General statistical terms and terms used in probability. ISO 3534-1:2006. Search in Google Scholar

[7] European co-operation for Accredidation. (2013). Evaluation of the uncertainty of measurement in calibration. EA-4/02 M:2013. Search in Google Scholar

[8] Balsamo, A., Mana, G., Pennecchi, F. (2006). The expression of uncertainty in non-linear parameter estimation. Metrologia, 43 (5), 396. https://doi.org/10.1088/0026-1394/43/5/009 Search in Google Scholar

[9] Bartel, T., Stoudt, S., Possolo, A. (2016). Force calibration using errors-in-variables regression and Monte Carlo uncertainty evaluation. Metrologia, 53 (3), 965. https://doi.org/10.1088/0026-1394/53/3/965 Search in Google Scholar

[10] Carroll, R. J., Ruppert, D., Stefanski, L. A., Crainiceanu, C. M. (2006). Measurement Error in Nonlinear Models: A Modern Perspective. Chapman and Hall/CRC, ISBN 9781584886334. Search in Google Scholar

[11] Casella, G., Berger, R. L. (2002). Statistical Inference, Second Edition. Duxbury, ISBN 0-534-24312-6. Search in Google Scholar

[12] Cecelski, C. E., Toman, B., Liu, F. H., Meija, J., Pos-solo, A. (2022). Errors-in-variables calibration with dark uncertainty. Metrologia, 59 (4), 045002. https://doi.org/10.1088/1681-7575/ac711c Search in Google Scholar

[13] Cox, M., Forbes, A., Harris, P., Smith, I. (2004). The classification and solution of regression problems for calibration. NPL Report CMSC 24/03, National Physical Laboratory, Teddington, UK. Search in Google Scholar

[14] Cox, D. R., Snell, E. J. (1968). A general definition of residuals. Journal of the Royal Statistical Society: Series B (Methodological), 30 (2), 248–265. Search in Google Scholar

[15] Fuller, W. A. (2009). Measurement Error Models. John Wiley & Sons, ISBN 9780470317334. Search in Google Scholar

[16] Guenther, F. R., Possolo, A. (2011). Calibration and uncertainty assessment for certified reference gas mixtures. Analytical and Bioanalytical Chemistry, 399 (1), 489–500. https://doi.org/10.1007/s00216-010-4379-z Search in Google Scholar

[17] Klauenberg, K., Martens, S., Bošnjaković, A., Cox, M. G., van der Veen, A. M., Elster, C. (2022). The GUM perspective on straight-line errors-in-variables regression. Measurement, 187, 110340. https://doi.org/10.1016/j.measurement.2021.110340 Search in Google Scholar

[18] Kotz, S., Nadarajah, S. (2004). Multivariate t Distributions and Their Applications. Cambridge University Press, ISBN 9780511550683. https://doi.org/10.1017/CBO9780511550683 Search in Google Scholar

[19] Kubáček, L. (1988). Foundations of Estimation Theory. Elsevier, ISBN 978-0444989413. Search in Google Scholar

[20] Kubáček, L. (1995). On a linearization of regression models. Applications of Mathematics, 40 (1), 61–78. Search in Google Scholar

[21] Kukush, A., Van Huffel, S. (2004). Consistency of elementwise-weighted total least squares estimator in a multivariate errors-in-variables model AX = B. Metrika, 59 (1), 75–97. https://doi.org/10.1007/s001840300272 Search in Google Scholar

[22] Krystek, M., Anton, M. (2007). A weighted total least-squares algorithm for fitting a straight line. Measurement Science and Technology, 18 (11), 3438. https://doi.org/10.1088/0957-0233/18/11/025 Search in Google Scholar

[23] Lira, I., Elster, C., Wöger, W. (2007). Probabilistic and least-squares inference of the parameters of a straight-line model. Metrologia, 44 (5), 379. https://doi.org/10.1088/0026-1394/44/5/014 Search in Google Scholar

[24] Malengo, A., Pennecchi, F. (2013). A weighted total least-squares algorithm for any fitting model with correlated variables. Metrologia, 50 (6), 654. https://doi.org/10.1088/0026-1394/50/6/654 Search in Google Scholar

[25] Markovsky, I., Van Huffel, S. (2007). Overview of total least-squares methods. Signal Processing, 87 (10), 2283–2302. https://doi.org/10.1016/j.sigpro.2007.04.004 Search in Google Scholar

[26] Milton, M., Harris, P., Smith, I., Brown, A., Goody, B. (2006). Implementation of a generalized least-squares method for determining calibration curves from data with general uncertainty structures. Metrologia, 43 (4), S291. https://doi.org/10.1088/0026-1394/43/4/S17 Search in Google Scholar

[27] Murphy, S. A., Van Der Vaart, A. W. (1996). Likelihood inference in the errors-in-variables model. Journal of Multivariate Analysis, 59 (1), 81–108. https://doi.org/10.1006/jmva.1996.0055 Search in Google Scholar

[28] Osborne, C. (1991). Statistical calibration: A review. International Statistical Review/Revue Internationale de Statistique, 59 (3), 309–336. https://doi.org/10.2307/1403690 Search in Google Scholar

[29] Possolo, A., Iyer, H. K. (2017). Invited article: Concepts and tools for the evaluation of measurement uncertainty. Review of Scientific Instruments, 88 (1), 011301. https://doi.org/10.1063/1.4974274 Search in Google Scholar

[30] National Physical Laboratory. (2010). Software to support ISO/TS 28037:2010E. https://www.npl.co.uk/resources/software/iso-ts-28037-2010e Search in Google Scholar

[31] Stoudt, S., Pintar, A., Possolo, A. (2021). Uncertainty evaluations from small datasets. Metrologia, 58 (1), 015014. https://doi.org/10.1088/1681-7575/abd372 Search in Google Scholar

[32] Witkovský, V. (2016). Numerical inversion of a characteristic function: An alternative tool to form the probability distribution of output quantity in linear measurement models. Acta IMEKO, 5 (3), 32–44. http://dx.doi.org/10.21014/acta_imeko.v5i3.382 Search in Google Scholar

[33] Witkovský, V., Wimmer, G. (2018). Generalized polynomial comparative calibration: Parameter estimation and applications. In Advances in Measurements and Instrumentation: Reviews, Vol. 1. IFSA Publishing, 15–52. ISBN 978-84-09-07321-4. Search in Google Scholar

[34] Witkovský, V., Wimmer, G. (2021). Exact confidence intervals for parameters in linear models with parameter constraints. In Measurement 2021: 13th International Conference on Measurement. Bratislava, Slovakia: Institute of Measurement Science, SAS, 22–25. https://doi.org/10.23919/Measurement52780.2021.9446783 Search in Google Scholar

[35] Witkovský, V., Wimmer, G. (2022). PolyCal – MATLAB algorithm for comparative polynomial calibration and its applications. In Advanced Mathematical and Computational Tools in Metrology and Testing XII: Series on Advances in Mathematics for Applied Sciences – Vol. 90. World Scientific, 501–512. https://doi.org/http://dx.doi.org/10.1142/9789811242380_0033 Search in Google Scholar

[36] Witkovský, V. (2022). CharFunTool: The characteristic functions toolbox (MATLAB). https://github.com/witkovsky/CharFunTool Search in Google Scholar

eISSN:
1335-8871
Langue:
Anglais
Périodicité:
6 fois par an
Sujets de la revue:
Engineering, Electrical Engineering, Control Engineering, Metrology and Testing