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Original Articles

The Completeness and Uniqueness of Johnson's System in Skewness–Kurtosis Space

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Pages 2097-2116 | Published online: 15 Feb 2007

References

  • DeBrota , D. J. , Dittus , R. S. , Swain , J. J. , Roberts , S. D. , Wilson , J. R. and Venkatraman , S. 1988 . “ Input modeling with the Johnson System of distributions ” . The 1988 Winter Simulation Conference 165 – 179 . Discusses the Johnson system and the methods (moment, percentile, least squares, minimum L P norm estimation) used to fit it. Software packages FITTR1 and VISIFT are illustrated via several examples
  • Draper , J. 1952 . Properties of distributions resulting from certain simple transformations of the normal distribution . Biometrika , 39 : 290 – 301 . Provides approximation formulas for ω and Ω as functions of β1 and β2
  • Dudewicz , E. J. and Mishra , S. N. 1988 . Modern Mathematical Statistics New York : John Wiley & Sons, Inc .
  • Galton , F. 1889 . Natural Inheritance London, England : MacMillan .
  • Hahn , G. J. and Shapiro , S. S. 1967 . Statistical Models in Engineering 198 – 200 . New York : John Wiley & Sons, Inc. .
  • Hill , I. D. , Hill , R. and Holder , R. L. 1976 . Fitting Johnson curves by moments . Appl. Statist. , 25 : 180 – 189 . A FORTRAN algorithm is presented. For S U , an iteration method is used to estimate parameters. For S B , approximation formulae due to Draper (1951) are applied to estimate parameters
  • Johnson , N. L. 1949a . Systems of frequency curves generated by methods of translation . Biometrika , 36 : 149 – 176 . This historic paper systematically introduces for the first time the transformed system which is now called the Johnson system, reviews historical development of normal distributions and discusses properties (median, mode, moments, shape and contact order of their probability density functions). Certain Pearson curves are also discussed. Iteration is used to obtain the moments for S B
  • Johnson , N. L. 1949b . Bivariate distributions based on simple translation systems . Biometrika , 36 : 297 – 304 . Gives a bivariate extension of the univariate Johnson system via z 1 = γ1 + δ1 f 1(y 1), z 2 = γ2 + δ2 f 2(y 2), where (z 1, z 2) have the joint normal bivariate distribution [PUBMED] [INFOTRIEVE]
  • Johnson , N. L. 1965 . Tables to facilitate fitting S U frequency curves . Biometrika , 52 : 547 – 571 . Tables present the values of γ and δ for β1 = 0.5(0.05)2 and for values of β2 at intervals of 0.1 and later 0.2 increasing from a point (in the (β1, β2) plane) near the lognormal line
  • Johnson , M. E. 1987 . Multivariate Statistical Simulation New York : John Wiley & Sons, Inc .
  • Johnson , N. L. and Kotz , S. 1970 . Continuous Univariate Distribution Functions Vol. 112 , 22 – 27 . New York : John Wiley & Sons, Inc. . Genesis and historical remarks are made about lognormal distributions, including moments, mode, median anddensity function's shape, and applications
  • Karian , Z. A. and Dudewicz , E. J. 2000 . Fitting Statistical Distributions: The Generalized Lambda Distribution and Generalized Bootstrap Methods Boca Raton : CRC Press . Shows how to fit distributions in a wide variety of circumstances, even when moments do not exist
  • Kendall , M. G. and Stuart , A. 1952 . The Advanced Theory of Statistics , Distribution Theory. 3rd ed. Vol. 1 , New York : Hafner Publishing Company .
  • Leslie , D. C. M. 1959 . Determination of parameters in the Johnson system of probability distributions . Biometrika , 46 : 229 – 243 . A method is suggested for calculating parameters if the distribution under study is not far removed from normality
  • Mage , D. T. 1980 . An explicit solution for S B parameters using four percentile points . Technometrics , 22 : 247 – 251 . Using percentile points corresponding to equidistant normal deviates, discusses the S B distribution and presents explicit solution for the parameters
  • Ord , J. K. 1972 . Families of Frequency Distributions London : Griffin . Collects major systems proposed in the literature, reviews their main properties and discusses the problems involved in selecting and fitting an appropriate model; includes continuous distributions, mixtures of distributions, a link between continuous and discrete, discrete models, and approximations to discrete distribution functions (linked with variance-stabilizing transforms)
  • Patel , J. K. and Read , C. B. 1996 . Handbook of the Normal Distribution , 2nd Revised and Expanded New York : Marcel Dekker, Inc .
  • Ramberg , J. S. , Tadikamalla , P. R. , Dudewicz , E. J. and Mykytka , E. F. 1979 . A probability distribution and its uses in fitting data . Technometrics , 21 ( 2 ) : 201 – 214 . Gives a generalization of Tukey's Lambda distribution different from the already-existing empirical distributions (the Pearson system, the Johnson system and the Burr distribution), that has simplicity, flexibility and generality; for details see Karian and Dudewicz (2000)
  • Slifker , J. F. and Shapiro , S. S. 1980 . The Johnson system: selection and parameter estimation . Technometrics , 22 ( 2 ) : 239 – 246 . Let x 3z , x z , x −z andx −3z be the values corresponding to 3z, z, −z, −3z under the Johnson transformation z = γ + δf((x − ξ)/λ), let m = x 3z  − x z , n = x −z  − x −3z andp = x z  − x −z . Then the ratio discriminates among the three distribution families: For S U , mn/p 2 > 1; for S B , mn/p 2 < 1; for S L , mn/p 2 = 1
  • Swain , J. J. , Venkatraman , S. and Wilson , J. R. 1988 . Least-squares estimation of distribution functions in Johnson's translation system . Statist. Comput. Simul. , 29 : 271 – 297 . Describes a least-squares procedure for distribution fitting involving a continuous target distribution function F(⋅) from which one has taken a random sample {X j  : 1 ≤ j ≤ n} with corresponding order statistics {X (j) : 1 ≤ j ≤ n}. Uses the software package FITTR1 to apply this technique to problems arising in medicine, applied statistics, and civil engineering. Contains an analysis of the results and relative performance of the three least-squares procedures

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