Skip to main content
Log in

On Statistical Methods for Common Mean and Reference Confidence Intervals in Interlaboratory Comparisons for Temperature

  • Published:
International Journal of Thermophysics Aims and scope Submit manuscript

Abstract

We consider a problem of constructing the exact and/or approximate coverage intervals for the common mean of several independent distributions. In a metrological context, this problem is closely related to evaluation of the interlaboratory comparison experiments, and in particular, to determination of the reference value (estimate) of a measurand and its uncertainty, or alternatively, to determination of the coverage interval for a measurand at a given level of confidence, based on such comparison data. We present a brief overview of some specific statistical models, methods, and algorithms useful for determination of the common mean and its uncertainty, or alternatively, the proper interval estimator. We illustrate their applicability by a simple simulation study and also by example of interlaboratory comparisons for temperature. In particular, we shall consider methods based on (i) the heteroscedastic common mean fixed effect model, assuming negligible laboratory biases, (ii) the heteroscedastic common mean random effects model with common (unknown) distribution of the laboratory biases, and (iii) the heteroscedastic common mean random effects model with possibly different (known) distributions of the laboratory biases. Finally, we consider a method, recently suggested by Singh et al., for determination of the interval estimator for a common mean based on combining information from independent sources through confidence distributions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. B. Arendacká, A simple confidence interval for the common mean, in Advanced Mathematical and Computational Tools in Metrology and Testing IX, ed. by F. Pavese, M. Bär, J.-R. Filtz, A. Forbes, L. Pendrill, K. Shirono (World Scientific Publishing Company, Singapore, 2012), pp. 13–17

  2. W. Bich, Bias and optimal linear estimation in comparison calibrations. Metrologia 29(1), 15 (1992)

    Article  ADS  MATH  Google Scholar 

  3. G. Casella, R.L. Berger, Statistical Inference, Advanced Series (Duxbury, Belmont, CA, 1999)

    Google Scholar 

  4. A.V. Chunovkina, C. Elster, I. Lira, W. Wöger, Analysis of key comparison data and laboratory biases. Metrologia 45, 211–216 (2008)

    Article  ADS  MATH  Google Scholar 

  5. P. Ciarlini, M.G. Cox, F. Pavese, G. Regoliosi, The use of a mixture of probability distributions in temperature interlaboratory comparisons. Metrologia 41(3), 116 (2004)

    Article  ADS  Google Scholar 

  6. CIPM MRA-D-05. Measurement comparisons in the CIPM MRA. Comité International des Poids et Mesures, BIPM, Paris, 2012

  7. CIPM MRA. Mutual recognition of national measurement standards and of calibration and measurement certificates issued by national metrology institutes. Comité International des Poids et Mesures, BIPM, Paris Technical Supplement, revised in October 2003 (1999)

  8. W. Cochran, Problems arising in the analysis of a series of similar experiments. J. R. Stat. Soc. 4(1), 102–118 (1937)

    Google Scholar 

  9. M.G. Cox, The evaluation of key comparison data. Metrologia 39(6), 589 (2002). This paper is the work of an international advisory group on uncertainties commissioned by the director of the BIPM

    Article  ADS  MATH  Google Scholar 

  10. M.G. Cox, P.M. Harris, The evaluation of key comparison data using key comparison reference curves. Metrologia 49(4), 437 (2012)

    Article  ADS  MATH  Google Scholar 

  11. R. DerSimonian, N. Laird, Meta-analysis in clinical trials. Control. Clin. Trials 7(3), 177–188 (1986)

    Article  MATH  Google Scholar 

  12. C. Elster, B. Toman, Analysis of key comparisons: Estimating laboratories’ biases by a fixed effects model using Bayesian model averaging. Metrologia 47, 113–119 (2010)

    Article  ADS  Google Scholar 

  13. C. Elster, B. Toman, Analysis of key comparison data: critical assessment of elements of current practice with suggested improvements. Metrologia 50(5), 549 (2013)

    Article  ADS  Google Scholar 

  14. C. Elster, A.G. Chunovkina, W. Wöger, Linking of a rmo key comparison to a related CIPM key comparison using the degrees of equivalence of the linking laboratories. Metrologia 47(1), 96 (2010)

    Article  ADS  Google Scholar 

  15. W.R. Fairweather, A method of obtaining an exact confidence interval for the common mean of several normal populations. Appl. Stat. 21(3), 229–233 (1972)

    Article  MathSciNet  Google Scholar 

  16. T. Friedrich, G. Knapp, Generalised interval estimation in the random effects meta regression model. Comput. Stat. Data Anal. 64, 165–179 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  17. F.A. Graybill, R.B. Deal, Combining unbiased estimators. Biometrics 15(4), 543–550 (1959)

    Article  MathSciNet  Google Scholar 

  18. A. Guolo, Higher-order likelihood inference in meta-analysis and meta-regression. Stat. Med. 31, 313–327 (2012)

    Article  MathSciNet  Google Scholar 

  19. J. Hannig, On generalized fiducial inference. Stat. Sin. 19, 491–544 (2009)

    MathSciNet  Google Scholar 

  20. J. Hartung, G. Knapp, On tests of the overall treatment effect in the metaanalysis with normally distributed responses. Stat. Med. 20, 1771–1782 (2001)

    Article  Google Scholar 

  21. J. Hartung, G. Knapp, Models for combining results of different experiments: retrospective and prospective. Am. J. Math. Manag. Sci. 25, 149–188 (2005)

    MathSciNet  Google Scholar 

  22. J. Hartung, G. Knapp, B. Sinha, Statistical Meta-Analysis with Applications (Wiley, Hoboken, NJ, 2008)

    Book  Google Scholar 

  23. H. Iyer, C. Wang, T. M, Models and confidence intervals for true values in interlaboratory trials. J. Am. Stat. Assoc. 99, 1060–1071 (2004)

    Article  Google Scholar 

  24. H.K. Iyer, C.M. Wang, D.F. Vecchia, Consistency tests for key comparison data. Metrologia 41(4), 223 (2004)

    Article  ADS  Google Scholar 

  25. JCGM100:2008 (GUM). Evaluation of measurement data—Guide to the expression of uncertainty in measurement (GUM 1995 with minor corrections). In JCGM—Joint Committee for Guides in Metrology. ISO (the International Organization for Standardization), BIPM, IEC, IFCC, ILAC, IUPAC, IUPAP and OIML, (2008)

  26. JCGM101:2008 (GUM S1). Evaluation of measurement data—Supplement 1 to the Guide to the expression of uncertainty in measurement—Propagation of distributions using a Monte Carlo method. In JCGM—Joint Committee for Guides in Metrology. ISO (the International Organization for Standardization), BIPM, IEC, IFCC, ILAC, IUPAC, IUPAP and OIML, (2008)

  27. S. Jordan, K. Krishnamoorthy, Exact confidence intervals for the common mean of several normal populations. Biometrics 52, 77–86 (1996)

    Article  Google Scholar 

  28. R. Kacker, R. Datla, A. Parr, Statistical interpretation of key comparison reference value and degrees of equivalence. J. Res. Natl. Inst. Stand. Technol. 108(6), 439–446 (2003)

    Article  Google Scholar 

  29. R.N. Kacker, Combining information from interlaboratory evaluations using a random effects model. Metrologia 41(3), 132 (2004)

    Article  ADS  Google Scholar 

  30. R.N. Kacker, R.U. Datla, A.C. Parr, Statistical analysis of cipm key comparisons based on the ISO guide. Metrologia 41(4), 340 (2004)

    Article  ADS  Google Scholar 

  31. R.N. Kacker, A. Forbes, R. Kessel, K.-D. Sommer, Bayesian posterior predictive p-value of statistical consistency in interlaboratory evaluations. Metrologia 45(5), 512 (2008)

    Article  ADS  Google Scholar 

  32. R.N. Kacker, A. Forbes, R. Kessel, K.-D. Sommer, Classical and Bayesian interpretation of the birge test of consistency and its generalized version for correlated results from interlaboratory evaluations. Metrologia 45(3), 257 (2008)

    Article  ADS  MATH  Google Scholar 

  33. M.G. Kenward, J.H. Roger, Small sample inference for fixed effects from restricted maximum likelihood. Biometrics 53, 983–997 (1997)

    Article  Google Scholar 

  34. G. Knapp, J. Hartung, Improved tests for a random effects meta-regression with a single covariate. Stat. Med. 22, 2693–2710 (2003)

    Article  Google Scholar 

  35. A. Koo, J.F. Clare, On the equivalence of generalized least-squares approaches to the evaluation of measurement comparisons. Metrologia 49(3), 340 (2012)

    Article  ADS  Google Scholar 

  36. K. Krishnamoorthy, Y. Lu, Inferences on the common mean of several normal populations based on the generalized variable method. Biometrics 59, 237–247 (2003)

    Article  MathSciNet  Google Scholar 

  37. M. Levenson, D. Banks, L. Gill, W. Guthrie, H. Liu, M. Vangel, J. Yen, N. Zhang, An ISO GUM approach to combining results from multiple methods. J. Res. Natl. Inst. Stand. Technol. 105, 571–579 (2000)

    Article  Google Scholar 

  38. I. Lira, Combining inconsistent data from interlaboratory comparisons. Metrologia 44(5), 415 (2007)

    Article  ADS  Google Scholar 

  39. P. Meier, Variance of a weighted mean. Biometrics 9, 59–73 (1953)

    Article  MathSciNet  Google Scholar 

  40. A.L. Rukhin, Confidence intervals for treatment effect from restricted maximum likelihood. J. Stat. Plan. Inference 142, 1999–2008 (2012)

    Article  MATH  Google Scholar 

  41. A. Rukhin, M. Vangel, Estimation of a common mean and weighted means statistics. J. Am. Stat. Assoc. 93, 303–308 (1998)

    Article  MathSciNet  Google Scholar 

  42. F.E. Satterthwaite, An approximate distribution of estimates of variance components. Biom. Bull. 2, 110–114 (1946)

    Article  Google Scholar 

  43. G. Sharma, T. Mathew, Higher order inference for the consensus mean in inter-laboratory studies. Biom. J. 53(1), 128–136 (2011)

    Article  MathSciNet  Google Scholar 

  44. K. Singh, M. Xie, W.E. Strawderman, Combining information from independent sources through confidence distributions. Ann. Stat. 33(1), 159–183 (2005)

    Article  MathSciNet  Google Scholar 

  45. M. Stock, S. Solve, D. del Campo, V. Chimenti, E. Méndez-Lango, H. Liedberg, P.P.M. Steur, P. Marcarino, R. Dematteis, E. Filipe, I. Lobo, K.H. Kang, K.S. Gam, Y.-G. Kim, E. Renaot, G. Bonnier, M. Valin, R. White, T.D. Dransfield, Y. Duan, Y. Xiaoke, G. Strouse, M. Ballico, D. Sukkar, M. Arai, A. Mans, M. de Groot, O. Kerkhof, R. Rusby, J. Gray, D. Head, K. Hill, E. Tegeler, U. Noatsch, S. Duris, H.Y. Kho, S. Ugur, A. Pokhodun, S.F. Gerasimov, Final report on CCT-K7: key comparison of water triple point cells. Metrologia 43(1A), 03001 (2006)

    Article  ADS  Google Scholar 

  46. B. Toman, Linear statistical models in the presence of systematic effects requiring a type B evaluation of uncertainty. Metrologia 43, 27–33 (2006)

    Article  ADS  Google Scholar 

  47. B. Toman, Statistical interpretation of key comparison degrees of equivalence based on distributions of belief. Metrologia 44(2), L14 (2007)

    Article  ADS  Google Scholar 

  48. B. Toman, A. Possolo, Laboratory effects models for interlaboratory comparisons. Accredit. Qual. Assur. 14, 553–563 (2009)

    Article  Google Scholar 

  49. W. Viechtbauer, Confidence intervals for the amount of heterogeneity in meta-analysis. Stat. Med. 26, 37–52 (2007)

    Article  MathSciNet  Google Scholar 

  50. C. Wang, H. Iyer, A generalized confidence interval for a measurand in the presence of type-A and type-B uncertainties. Measurement 39, 856–863 (2006)

    Article  Google Scholar 

  51. B.L. Welch, The generalization of “Student’s” problem when several different population variances are involved. Biometrika 34, 28–35 (1947)

    MathSciNet  Google Scholar 

  52. D.R. White, On the analysis of measurement comparisons. Metrologia 41(3), 122 (2004)

    Article  ADS  Google Scholar 

  53. R. Willink, An improved procedure for combining Type A and Type B components of measurement uncertainty. Int. J. Metrol. Qual. Eng. 4, 55–62 (2013)

    Article  Google Scholar 

  54. R. Willink, Measurement Uncertainty and Probability (Cambridge University Press, New York, 2013)

    Book  Google Scholar 

  55. V. Witkovský Matlab algorithm TDIST: the distribution of a linear combination of student’s t random variables. In: Proceedings of the COMPSTAT 2004 Symposium, J. Antoch, ed., (Springer, Heidelberg, 2004) pp. 1995–2002

  56. V. Witkovský, A. Savin, G. Wimmer, On small sample inference for common mean in heteroscedastic one-way model. Discuss. Math. Prob. Stat. 23(2), 123–145 (2003)

    Google Scholar 

  57. V. Witkovský, Comparison of some exact and approximate interval estimators for common mean. Meas. Sci. Rev. 5(1), 19–22 (2005)

    Google Scholar 

  58. V. Witkovský, G. Wimmer, Confidence interval for common mean in interlaboratory comparisons with systematic laboratory biases. Meas. Sci. Rev. 7, 64–73 (2007)

    Google Scholar 

  59. M. Xie, K. Singh, W.E. Strawderman, Confidence distributions and a unifying framework for meta-analysis. J. Am. Stat. Assoc. 106(493), 320–333 (2011)

    Article  MathSciNet  Google Scholar 

  60. M. Xie, K. Singh, Confidence distribution, the frequentist distribution estimator of a parameter: a review (with discussions). Int. Stat. Rev. 81, 3–39 (2013)

    Article  MathSciNet  Google Scholar 

  61. P.L. Yu, Y. Sun, B.K. Sinha, On exact confidence intervals for the common mean of several normal populations. J. Stat. Plan. Inference 81, 263–277 (1999)

    Article  MathSciNet  Google Scholar 

  62. N.F. Zhang, Statistical analysis for interlaboratory comparisons with linear trends in multiple loops. Metrologia 49(3), 390 (2012)

    Article  ADS  Google Scholar 

  63. W. Zhang, N.F. Zhang, H. kung Liu, A generalized method for the multiple artefacts problem in interlaboratory comparisons with linear trends. Metrologia 46(3), 345 (2009)

    Article  ADS  Google Scholar 

Download references

Acknowledgments

We would like to thank the editors and anonymous reviewers for their valuable suggestions which helped significantly to improve the quality of the presented material. The work was supported by the Slovak Research and Development Agency, Grant APVV-0096-10, and by the Scientific Grant Agency of the Ministry of Education of the Slovak Republic and the Slovak Academy of Sciences, Grants VEGA 2/0047/15 and VEGA 1/0604/15.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Viktor Witkovský.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Witkovský, V., Wimmer, G. & Ďuriš, S. On Statistical Methods for Common Mean and Reference Confidence Intervals in Interlaboratory Comparisons for Temperature. Int J Thermophys 36, 2150–2171 (2015). https://doi.org/10.1007/s10765-015-1917-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10765-015-1917-0

Keywords

Navigation