Open Access
Int. J. Metrol. Qual. Eng.
Volume 8, 2017
Article Number 29
Number of page(s) 29
Published online 27 November 2017
  1. BIPM, IEC, IFCC, ILAC, ISO, IUPAC, IUPAP, and OILM, Guide to the expression of uncertainty in measurement. Technical report, Bureau International de Poids et Mesures (BIPM), 2008, [Google Scholar]
  2. BIPM, IEC, IFCC, ILAC, ISO, IUPAC, IUPAP, and OILM, Evaluation of Measurement data − Supplement 1 to the “Guide to the expression of uncertainty in measurement” − propagation of distributions using a Monte Carlo method. Technical report, Bureau International de Poids et Mesures (BIPM), 2008, [Google Scholar]
  3. P.M. Harris, C.E. Matthews, M.G. Cox, A.B. Forbes, Summarizing the output of a Monte Carlo Method for uncertainty evaluation, Metrologia 51, 243–252 (2014) [Google Scholar]
  4. V. Ramnath, Application of quantile functions for the analysis and comparison of gas pressure balance uncertainties, Int. J. Metrol. Qual. Eng. 8, 1–18 (2017) [CrossRef] [EDP Sciences] [Google Scholar]
  5. BIPM, IEC, IFCC, ILAC, ISO, IUPAC, IUPAP, and OILM, Evaluation of measurement data − supplement 2 to the “Guide to the expression of uncertainty in measurement” − extension to any number of output quantities. Technical report, Bureau International de Poids et Mesures (BIPM), 2011, [Google Scholar]
  6. R.S. Dadson, S.L. Lewis, G.N. Peggs, The Pressure Balance: Theory and Practice ( HMSO, London, 1982) [Google Scholar]
  7. A. Picard, R.S. Davis, M. Glaser, K. Fujii, Revised formula for the density of moist air (CIPM-2007), Metrologia 45, 149–159 (2008) [Google Scholar]
  8. M. Dehghan, M. Hajarian, Some iterative free quadratic and cubic convergence iterative formulas for solving nonlinear equations, Comput. Appl. Math. 29, 19–30 (2010) [CrossRef] [Google Scholar]
  9. W. Bich, Interdependence between measurement uncertainty and metrological traceability, Accredit. Qual. Assur. 14, 581–586 (2009) [CrossRef] [Google Scholar]
  10. M.G. Cox, P.M. Harris, Software support for metrology best practice guide no. 6–uncertainty evaluation. Technical report, National Physical Laboratory (United Kingdom), 2006. NPL Report DEM-ES-011 (ISSN 1744-0475), [Google Scholar]
  11. R. Palencar, G. Wimmer, M. Halaj, Determination of the uncertainties and covariances in the calibration of the set of weights, Meas. Sci. Review 2, 9–20 (2002) [Google Scholar]
  12. EURAMET, Calibration of Pressure Balances. Technical report, European Association of National Metrology Istitutes, 2011. Guide 3 Version 1.0, ISBN 978-3-942992-02-2 [Google Scholar]
  13. W. Sabuga, G. Molinar, G. Buonanno, T. Esward, J.C. Legras, Finite element method used for calculation of the distortion coefficient and associated uncertainty of a PTB 1 GPa pressure balance − EUROMET project 463, Metrologia 43, 311–625 (2006) [Google Scholar]
  14. S. Dogra, S. Yadav, A.K. Bandyopadhyay, Computer simulation of a 1.0 GPa piston-cylinder assembly using finite element analysis (FEA), Measurement 43, 1345–1354 (2010) [CrossRef] [Google Scholar]
  15. M.G. Cox, B.R.L. Siebert, The use of a Monte Carlo method for evaluating uncertainty and expanded uncertainty, Metrologia 43, S178–S188 (2006) [Google Scholar]
  16. R. Willink, Representing Monte Carlo output distributions for transferability in uncertainty analysis: modelling with quantile functions, Metrologia 46, 154–166 (2009) [Google Scholar]
  17. K. Goda, Statistical modeling of joint probability distribution using copula: application to peak and permanent displacement seismic demands, Struct Saf. 32, 112–123 (2010) [CrossRef] [Google Scholar]
  18. Scipy v0.19.0 reference guide − scipy.interpolate.bspline, (accessed: 2017 /02/04). [Google Scholar]
  19. V. Ramnath, Determination of pressure balance distortion coefficient and zero-pressure effective area uncertainties, Intern. J. Metrology Quality Eng. 2, 101–119 (2011) [Google Scholar]
  20. M. Krystek, M. Anton, A least-squares algorithm for fitting data points with mutually correlated coordinates to a straight line, Meas. Sci. Technol. 22, 035101 (2011) [Google Scholar]
  21. X.-S. Tang, D.-Q. Li, C.-B. Zhou, K.-K. Phoon, L.-M. Zhang, Impact of copulas for modelling bivariate distributions on system reliability, Struct. Saf. 44, 80–90 (2013) [CrossRef] [Google Scholar]
  22. C. Genest, A.-C. Favre, Everything you always wanted to know about copula modeling but were afraid to ask, J. Hydrol. Eng. 12, 347–368 (2007) [Google Scholar]
  23. X. He, P. Ng, S. Portnoy, Bivariate quantile smoothing splines, J. R. Stat. Soc. Ser. B: Stat. Methodol. 60, 537–550 (1998) [CrossRef] [Google Scholar]
  24. D.H. Bailey, J.M. Borwein, Hight-precision arithmetic in mathematical physics, Mathematics 3, 337–367 (2015) [CrossRef] [Google Scholar]
  25. D.R. White, P. Saunders, The propogation of uncertainty with calibration equations, Meas. Sci. Technol. 18, 2157–2169 (2007) [Google Scholar]
  26. W.H. Press, S.A. Teukolsky, W.T. Vetterling, B.P. Flannery, in Numerical Recipes: The Art of Scientific Computing, 3rd edn. (Cambridge University Press, 2007) [Google Scholar]
  27. W.G. Gilchrist, in Statistical Modelling with Quantile Functions, edited by Chapman and Hall, 1st edn. (2000) ISBN 1-58488- 174–7 [CrossRef] [Google Scholar]
  28. P. Chaudhuri, On a geometric notion of quantiles for multivariate data, J. Am. Stat. Assoc. 91, 862–872 (1996), [Google Scholar]
  29. H. Kajikawa, T. Kobata, K. Ide, A. Ooiwa. Precise determination of the jacket pressure coefficient of controlled clearance pressure balances, in 3rd IMEKO TC16 International Conference on Pressure Measurement (Curran Associates, Inc., 2007), p. 125–134, ISBN 978-1-61567-635-4 [Google Scholar]
  30. H. Kajikawa, K. Ide, T. Kobata, Methods of precisely estimating the jacket pressure coefficient of controlled clearance piston cylinders at pressures up to 1 GPa, Metrologia 48, 352–358 (2011) [Google Scholar]
  31. I. Kocas, W. Sabuga, M. Bergoglio, A. Eltaweel, C. Korasie, P. Farar, J. Setina, B. Waller, Y. Durgut, Final report on key comparison euramet.m.p-k13 in the range 50 mpa to 500 mpa of hydraulic gauge pressure, Metrologia 52, 07008 (2015) [Google Scholar]
  32. U. Diwekar, A. David, in BONUS Algorithm for Large Scale Stochastic Nonlinear Programming Problems, edited by U. Diwekar, A. David (Springer, 2015), pp. 27–34, ISBN 978-1-4939-2281-9, [Google Scholar]
  33. E. Larsson, B. Fornberg, A numerical study of some radial basis function based solution methods for elliptic PDEs, Comput. Math. Appl. 46, 891–902 (2003) [Google Scholar]
  34. I. Georgieva, C. Hofreither, An algorithm for low-rank approximation of bivariate functions using splines, J. Comput. Appl. Math. (2017) 310, 80–91, DOI:10.1016/ [Google Scholar]
  35. X. Luo, P.V. Shevchenko, The t copula with multiple parameters of degrees of freedom: bivariate charactersistics and application to risk management, Quant. Finance 10, 1039–1054 (2010) [Google Scholar]
  36. J. Yan, Enjoy the joy of copulas: with a package copula, J. Stat. Softw. 21, 1–21 (2007) [EDP Sciences] [Google Scholar]
  37. I. Kojadinovic, J. Yan, Modelling multivariate distributions with continous margins using the copula R package, J. Stat.Softw. 34, 1–20 (2010) [CrossRef] [Google Scholar]
  38. M. Hofert, I. Kojadinovic, M. Maechler, J. Yan, The Comprehensive R Archive Network − Multivariate Dependence with Copulas Version 0. 999-15, 2017, (accessed: 05/29/ 2017) [Google Scholar]
  39. W. Asquith, The Comprehensive R Archive network − General Bivariate Copula Theory and Many Utility Functions Version 2.0.4, 2017, (accessed: 05/29/2017) [Google Scholar]
  40. U. Schepsmeier, E. C. Brechmann, B. Graeler, T. Nagler, T. Erhardt, C. Almeida, A. Min, C. Czado, M. Hofmann, M. Killiches, H. Joe, T. Vatter, The Comprehensive R Archive Network − Statistical Inference of Vine Copulas Version 2.1.2, 2017, (accessed: 05/29/2017) [Google Scholar]
  41. C. Meyer, The bivariate normal copula, Commun. Stat.: Theory Methods 42, 2402–2422 (2013) [CrossRef] [Google Scholar]
  42. J. Segers, M. Sibuya, H. Tsukahara, The empirical beta copula, J. Mult ivar. Anal. 155, 35–51 (2017) [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.