- AwadF. (2007). Characterization of the statistical properties of Response Modeling Methodology (RMM), and comparing the effectiveness of systems of distributions (including RMM) as a general model for distribution fitting. PhD final report. Supervisor: Haim Shore. Department of Industrial Engineering and Management, Ben-Gurion Univeristy.
- Benson-KarhiD., ShoreH., ShachamM. (2007). Modeling temperature-dependent properties of water via response modeling methodology (RMM) and comparison with acceptable models. Industrial & Engineering Chemistry Research. 46(10), 3446–3463.
- BurnhamK. P., AndersonD. R. (2002). Model Selection and Multimodel Inference- A Practical Information-theoretic Approach. 2nd edition. Springer. NY.
- ClementsJ. A. (1989). Process capability calculations for non-normal distributions. Quality Progress, 22(2), 49–55.
- DudewiczE. J., LevyG. C., LienhartJ.L., WehrliF. (1989). Statistical analysis of magnetic resonance imaging data in the normal brain dData (screening normality, discrimination, variability), and implications for expert statistical programming for ESS™ (the Expert Statistical System). American Journal of Mathematical and Management Sciences, 9, 299–359.
- KarianZ. A., DudewiczE. J. (2000). Fitting Statistical Distributions: The Generalized Lambda Distribution and Generalized Bootstrap Methods. CRC Press, Boca Raton, Florida, USA.
- KarianZ. A., DudewiczE. J. (2003). Comparison of GLD fitting methods: Superiority of percentile fits to moments in L2 norm. Journal of the Iranian Statistical Society, 2(2), 171–187.
- LadanyS., ShoreH. (2007). Profit maximizing warranty period with sales expressed by a demand function. Quality and Reliability Engineering International. 23, 3, 291–301.
- ShoreH. (1998). A new approach to analysing non-normal quality data with application to process capability analysis. International Journal of Production Research (IJPR), 36(7), 1917–1933.
- ShoreH. (2003). Response Modeling Methodology (RMM) — A new approach to model a chemo-response for a monotone convex/concave relationship. Computers and Chemical Engineering, 27(5), 715–726.
- ShoreH. (2004a). Response Modeling Methodology (RMM) - Current distributions, transformations and approximations as special cases of the RMM error distribution. Communications in Statistics (Theory & Methods), 33(7), 1491–1510.
- ShoreH. (2004b). Non-normal populations in quality applications - A revisited perspective. Quality and Reliability Engineering International, 20(4), 375–382.
- ShoreH. (2005). Response Modeling Methodology- Empirical Modeling for Engineering and Science. World Scientific Publishing Co. Ltd., Singapore.
- ShoreH. (2007). Comparison of Generalized Lambda Distribution (GLD) and Response Modeling Methodology (RMM) as general platforms for distribution fitting. Communications in Statistics (Theory & Methods). 36 (15).
- ShoreH., Benson-KarhiD. (2007). Forecasting S-shaped diffusion processes via response modeling methodology. Journal of the Operational Research Society. 58, 6, 720–729.
- ShoreH., AwadF. (2007). Comparison of five families of distributions as general platforms for distribution fitting. Submitted.
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