Comparison of Generalized Lambda Distribution (GLD) and Response Modeling Methodology (RMM) as General Platforms for Distribution Fitting

References

• Clements , J. A. ( 1989 ). Process capability calculations for non-normal distributions . Qual. Prog. 22 : 95 – 100 .
   Google Scholar
• Karian , Z. A. , Dudewicz , E. J. ( 2000 ). Fitting Statistical Distributions: The Generalized Lambda Distribution and Generalized Bootstrap Methods . Boca Raton, FL : CRC Press .
   Google Scholar
• Karian , Z. A. , Dudewicz , E. J. ( 2003 ). Comparison of GLD fitting methods: superiority of percentile fits to moments in L 2 norm . J. Iranian Statist. Soc. 2 ( 2 ): 171 – 187 .
   Google Scholar
• Kotz , S. , Johnson , N. L. ( 2002 ). Process capability indices – a review, 1992–2000 . J. Qual. Technol. 34 ( 1 ): 2 – 19 .
   Web of Science ®Google Scholar
• Ladany , S. , Shore , H. ( 2007 ). Profit maximizing warranty period with sales expressed by a demand function . Qual. Reliab. Eng. Int. 23 : 291 – 301 .
   Web of Science ®Google Scholar
• Pearson , E. S. , Johnson , N. L. , Burr , I. W. ( 1979 ). Comparisons of the percentage points of distributions with the same first four moments, chosen from eight different systems of frequency curves . Commun. Statist. Simul. Computat. B8 ( 3 ): 191 – 229 .
   Google Scholar
• Shore , H. ( 1998 ). A new approach to analysing non-normal quality data with application to process capability analysis . Int. J. Product. Res. (IJPR) V. 36 ( 7 ): 1917 – 1933 .
   Web of Science ®Google Scholar
• Shore , H. ( 2004 ). Response Modeling Methodology (RMM) – Current distributions, transformations and approximations as special cases of the RMM error distribution . Commun. Statist. Theor. Meth. 33 ( 7 ): 1491 – 1510 .
   Web of Science ®Google Scholar
• Shore , H. ( 2005 ). Response Modeling Methodology – Empirical Modeling for Engineering and Science . Singapore : World Scientific Publishing Co. Ltd .
   Google Scholar
• Shore , H. , Benson-Karhi , D. ( 2007 ). Forecasting S-shaped diffusion processes via response modeling methodology . J. Operat. Res. Soc. 58 : 720 – 729 .
   Web of Science ®Google Scholar