Open Access
Issue
Int. J. Metrol. Qual. Eng.
Volume 4, Number 3, 2013
Page(s) 185 - 191
DOI https://doi.org/10.1051/ijmqe/2013053
Published online 06 March 2014
  1. R.A. Johnson, Miller and Freund’s Probability and Statistics for Engineers, 5th edn. (Prentice-Hall, London, 1994) [Google Scholar]
  2. G. Barbato, E.M. Barini, G. Genta, R. Levi, Features and performances of some outlier detection methods, J. Appl. Stat. 38, 2133–2149 (2011) [CrossRef] [Google Scholar]
  3. V. Barnett, T. Lewis, Outliers in Statistical Data, 3rd edn. (John Wiley, Chichester, 1994) [Google Scholar]
  4. H.A. David, Order Statistics, 2nd edn. (John Wiley, New York, 1981) [Google Scholar]
  5. L.G. Johnson, Theory and Technique of Variation Research (Elsevier, Amsterdam, 1964) [Google Scholar]
  6. G. Genta, Methods for Uncertainty Evaluation in Measurement (VDM, Saarbrücken, 2010) [Google Scholar]
  7. JCGM 100:2008. Evaluation of measurement data – Guide to the expression of uncertainty in measurement (GUM), BIPM-JCGM, Sèvres [Google Scholar]
  8. G. Barbato, G. Genta, A. Germak, R. Levi, G. Vicario, Treatment of experimental data with discordant observations: issues in empirical identification of distribution, Meas. Sci. Rev. 12, 133–140 (2012) [CrossRef] [Google Scholar]
  9. T.M. Porter, The Rise of Statistical Thinking 1820–1900 (Princeton University Press, Princeton, 1986) [Google Scholar]
  10. L.A.J. Quetelet, Du système social et des lois qui le régissent (Guillaumin & C., Paris, 1848) [Google Scholar]
  11. L.A.J. Quetelet, Physique sociale, ou essai sur le développement des facultés de l’homme (Muquardt, Bruxelles, 1869) [Google Scholar]
  12. F. Galton, The geometric mean, in vital and social statistics, Proc. Roy. Soc. 29, 365–367 (1879) [CrossRef] [Google Scholar]
  13. D. McAlister, The Law of the Geometric Mean, Proc. Roy. Soc. 29, 367–376 (1879) [CrossRef] [Google Scholar]
  14. E. Limpert, W.A. Stahel, M. Abbt, Log-normal Distributions across the Sciences: Keys and Clues, BioScience 51, 341–352 (2001) [CrossRef] [Google Scholar]
  15. M. Abramowitz, I.A. Stegun, Handbook of Mathematical Functions, Applied Mathematics Series (National Bureau of Standards, Washington, 1964), Vol. 55 [Google Scholar]
  16. W.F.R. Weldon, On certain correlated Variations in Carcinus moenas, Proc. Roy. Soc. 54, 318–329 (1893) [CrossRef] [Google Scholar]
  17. K. Pearson, Contributions to the mathematical theory of evolution, Philos. Trans. Roy. Soc. Lond. Ser. A 185, 71–110 (1894) [CrossRef] [Google Scholar]
  18. K. Pearson, Mathematical contributions to the theory of evolution, XIX: Second supplement to a memoir on skew variation, Philos. Trans. Roy. Soc. Lond. Ser. A 216, 429–457 (1916) [CrossRef] [Google Scholar]
  19. J.K. Ord, Families of Frequency Distributions (Griffin, London, 1972) [Google Scholar]
  20. W.P. Elderton, Frequency curves and correlation, 4th edn. (Cambridge University Press, Cambridge, 1953) [Google Scholar]
  21. A. Rhind, Tables to facilitate the computation of the probable errors of the chief constants of skew frequency distributions, Biometrika 7, 127–147 (1909) [CrossRef] [Google Scholar]
  22. G.J. Hahn, S.S. Shapiro, Statistical Models in Engineering (John Wiley, New York, 1958) [Google Scholar]
  23. N.L. Johnson, E. Nixon, D.E. Amos, Tables of percentage points of Pearson curves for given Formula , , expressed in standard measure, Biometrika 50, 459–471 (1963) [Google Scholar]
  24. O. Podladchikova, B. Lefebvre, V. Krasnoselskikh, V. Podladchikov, Classification of probability densities on the basis of Pearson’s curves with application to coronal heating simulations, Nonlin. Process. Geophys. 10, 323–333 (2003) [CrossRef] [Google Scholar]
  25. I.W. Burr, Cumulative frequency functions, Ann. Math. Stat. 13, 215–232 (1942) [CrossRef] [Google Scholar]
  26. R.N. Rodriguez, A guide to Burr Type XII distributions, Biometrika 64, 129–134 (1977) [CrossRef] [Google Scholar]
  27. D.R. Wingo, Maximum Likelihood Methods for Fitting the Burr Type XII Distribution to Multiply (Progressively) Censored Life Test Data, Metrika 40, 201–210 (1993) [CrossRef] [Google Scholar]
  28. F.Y. Edgeworth, On the Representation of Statistics by Mathematical Formulae, Part I, J. Roy. Statist. Soc. 61, 670–700 (1898) [Google Scholar]
  29. N.L. Johnson, System of Frequency Curves Generated by Methods of Translation, Biometrika 36, 149–178 (1949) [MathSciNet] [PubMed] [Google Scholar]
  30. D.J. DeBrota, R.S. Dittus, S.D. Roberts, J.R. Wilson, Visual interactive fitting of bounded Johnson distributions, Trans. Soc. Model. Simul. Int. – Simulation 52, 199–205 (1989) [CrossRef] [Google Scholar]
  31. J.D. Hill, R. Hill, R.L. Holder, Fitting Johnson Curves by Moments, Appl. Stat. 25, 190–192 (1976) [CrossRef] [Google Scholar]
  32. R.E. Wheeler, Quantile estimators of Johnson curve parameters, Biometrika 67, 725–728 (1980) [CrossRef] [Google Scholar]
  33. J.J. Swain, S. Venkatraman, J.R. Wilson, Least-Squares Estimation of Distribution Function in Johnson’s Translation System, J. Statist. Comput. Simul. 29, 271–297 (1988) [CrossRef] [Google Scholar]
  34. J. Bukaè, Fitting curves using symmetrical percentile points, Biometrika 59, 688–690 (1972) [Google Scholar]
  35. D.T. Mage, An Explicit Solution for Parameters Using Four Percentile Points, Technometrics 22, 247–251 (1980) [Google Scholar]
  36. J.F. Slifker, S.S. Shapiro, The Johnson System: Selection and Parameter Estimation, Technometrics 22, 239–246 (1980) [CrossRef] [Google Scholar]
  37. J.W. Tukey, The practical relationship between the common transformations of percentages of counts and of amounts, Technical Report. No. 36, Statistical Techniques Research Group (Princeton University, Princeton, 1960) [Google Scholar]
  38. J.J. Filliben, The Probability Plot Correlation Coefficient Test for Normality, Technometrics 17, 111–117 (1975) [CrossRef] [Google Scholar]
  39. NIST/SEMATECH 2013. e-Handbook of Statistical Methods, http://www.itl.nist.gov/div898/handbook/ [Google Scholar]
  40. A. Tarsitano, Fitting the Generalized Lambda Distribution to Income Data. COMPSTAT 2004Proceedings in Computational Statistics 16th Symposium, Prague, 2004 [Google Scholar]
  41. J.S. Ramberg, B.W. Schmeiser, An approximate method for generating symmetric random variables, Commun. Assoc. Comput. Mach. 15, 987–990 (1972) [Google Scholar]
  42. J.S. Ramberg, B.W. Schmeiser, An approximate method for generating asymmetric random variables, Commun. Assoc. Comput. Mach. 17, 78–82 (1974) [Google Scholar]
  43. S. Pal, Evaluation of Non-normal Process Capability Indices using Generalized Lambda Distribution, Qual. Eng. 17, 77–85 (2005) [CrossRef] [Google Scholar]
  44. B.W. Silverman, Using Kernel Density Estimates to Investigate Multimodality, J. Roy. Statist. Soc. Ser. B 43, 97–99 (1981) [Google Scholar]
  45. B. Efron, Bootstrap methods: Another look at the jackknife, Ann. Statist. 7, 1–26 (1979) [CrossRef] [MathSciNet] [Google Scholar]
  46. R. Schmitt, P. Fritz, J. Lose, Bootstrap approach for conformance assessment of measurement, Int. J. Metrol. Qual. Eng. 2, 19–24 (2011) [CrossRef] [EDP Sciences] [Google Scholar]
  47. N.R. Draper, H. Smith, Applied Regression Analysis (John Wiley, New York, 1966) [Google Scholar]
  48. F. Bookstein, Fitting conic sections to scattered data, Comput. Graph. Image Process. 9, 56–71 (1987) [CrossRef] [Google Scholar]
  49. P. O’Leary, P. Zsombor-Murray, Direct and specific least-square fitting of hyperbolae and ellipses, J. Electron. Imag. 13, 492–503 (2004) [CrossRef] [Google Scholar]
  50. W.J. Youden, Enduring values, Technometrics 14, 1–11 (1972) [CrossRef] [Google Scholar]
  51. M.H. DeGroot, A Conversation with George Box, Stat. Sci. 2, 239–258 (1987) [CrossRef] [Google Scholar]
  52. J.W. Tukey, The Future of Data Analysis, Ann. Math. Statist. 33, 1–47 (1962) [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.