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Original Articles

The Johnson System: Selection and Parameter Estimation

James F. Slifker Department of Mathematical Sciences, Florida International University, Miami, FL, 33199
&
Samuel S. Shapiro Department of Mathematical Sciences, Florida International University, Miami, FL, 33199
Pages 239-246 | Published online: 23 Mar 2012

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F. Yuan, X. H. Li, X. Y. Bai, S. M. Jowitt, M. M. Zhang, C. Jia & T. F. Zhou. (2010) Comparison of normalisation methods for non-normal distributed soil geochemical data: a case study from the Tongling metallogenic district, Yangtze belt, Anhui Province, China. Applied Earth Science 119:4, pages 227-235.
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MichaelR. Flynn. (2010) Constrained Maximization of the Shapiro–Wilk W Statistic to Estimate Parameters of the Johnson S Distribution. Communications in Statistics - Theory and Methods 39:20, pages 3635-3644.
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Stefan Niermann. (2006) Evolutionary estimation of parameters of Johnson distributions. Journal of Statistical Computation and Simulation 76:3, pages 185-193.
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Edward J. Dudewicz, Charles X. Zhang & Zaven A. Karian. (2004) The Completeness and Uniqueness of Johnson's System in Skewness–Kurtosis Space. Communications in Statistics - Theory and Methods 33:9, pages 2097-2116.
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John J. Flaig. (2002) Process Capability Optimization. Quality Engineering 15:2, pages 233-242.
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Hans J.H. Tuenter. (2001) An algorithm to determine the parameters of S -curves in the johnson system of probabillity distributions by moment matching . Journal of Statistical Computation and Simulation 70:4, pages 325-347.
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Hanfeng Chen & Grazyna. Kamburowska. (2001) Fitting data to the johnson system. Journal of Statistical Computation and Simulation 70:1, pages 21-32.
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Alan M. Polansky, Youn-Min Chou & Robert L. Mason. (1999) An Algorithm for Fitting Johnson Transformations to Non-Normal Data. Journal of Quality Technology 31:3, pages 345-350.
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AlanM. Polansky, Youn-Min Chou & RobertL. Mason. (1998) ESTIMATING PROCESS CAPABILITY INDICES FOR A TRLTNCATED DISTRIBUTION. Quality Engineering 11:2, pages 257-265.
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Youn-Min Chou, Alan M. Polansky & Robert L. Mason. (1998) Transforming Non-Normal Data to Normality in Statistical Process Control. Journal of Quality Technology 30:2, pages 133-141.
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NicholasR. Farnum. (1996) USING JOHNSON CURVES TO DESCRIBE NON-NORMAL ROCESS DATA. Quality Engineering 9:2, pages 329-336.
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A Louise Swift. (1994) Experience with a criterion for the classification of johnson distributions. Communications in Statistics - Simulation and Computation 23:4, pages 1127-1136.
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Youn-Min Chou, S Turner, S Henson, D Meyer & K. S Chen. (1994) On using percentiles to fit data by a johnson distribution. Communications in Statistics - Simulation and Computation 23:2, pages 341-354.
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H.L. Mac Gillivray. (1992) Shape properties of the g-and-h and johnson families. Communications in Statistics - Theory and Methods 21:5, pages 1233-1250.
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Donald P. Strickert. (1990) Estimating consumer acceptance limits. Communications in Statistics - Theory and Methods 19:7, pages 2365-2472.
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Mohammed A. Shayib. (1989) The Procedure for Selection of Transformations from the Johnson System. Communications in Statistics - Simulation and Computation 18:4, pages 1457-1464.
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K. O. Bowman & L. R. Shenton. (1989) S and S distributions fitted by percentiles a general criterion. Communications in Statistics - Simulation and Computation 18:1, pages 1-13.
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K. O. Bowman & L. R. Shenton. (1988) Solutions to Johnson's SB and SU . Communications in Statistics - Simulation and Computation 17:2, pages 343-348.
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D. B. Owen & Huaixiang Li. (1988) The starship for point estimates and confidence intervals on a mean and for percentiles. Communications in Statistics - Simulation and Computation 17:2, pages 325-341.
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DavidT. Mage. (1980) An Explicit Solution for SB, Parameters Using Four Percentile Points. Technometrics 22:2, pages 247-251.
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