1 Shore, H. Response Modeling Methodology (RMM): Empirical Modeling for Engineering and Science. Singapore: World Scientific Publishing Co. Ltd.
; 2005, 435.
2 Shore, H. Response modeling methodology (RMM)—validating evidence from engineering and the sciences. Qual Reliab Eng Int 2004, 20:61–79.
3 Shore, H, Benson‐Karhi, , D. Forecasting S‐shaped diffusion processes via response modeling methodology. J Oper Res Soc 2007, 58:720–729.
4 Shore, H. Comparison of generalized lambda distribution (GLD) and response modeling methodology (RMM) as general platforms for distribution fitting. Commun Stat (Theory Methods) 2007, 36:2805–2819.
5 Shore, H. Accurate RMM‐based approximations for the CDF of the normal distribution. Commun Stat (Theory Methods) 2005, 34:507–513.
6 Shore, H. %22Distribution fitting with the quantile function of Response Modeling Methodology.%22 In: Karian, ZA, Dudewicz, EJ, eds. Handbook of Fitting Statistical Distributions with R. Boca Raton, FL: Chapman %26 Hall/CRC, Taylor %26 Francis Group
; 2010, 537–556.
7 Shore, H, A`wad, F. Statistical comparison of the goodness‐of‐fit delivered by five families of distributions used in distribution fitting. Commun Stat (Theory Methods) 2010, 39:1707–1728.
8 Shauly, M, Shore, H, Parmet, Y. Comparison of Pearson distribution system and Response Modeling Methodology (RMM) as models for distribution fitting. Submitted for publication.
9 Shore, H. Comparison of linear predictors obtained by data transformation, generalized linear models (GLM) and response modeling methodology (RMM). Qual Reliab Eng Int 2008, 24:389–399.
10 Shore, H, Shauly, M, Parmet, Y. Process capability analysis for skewed process distributions based on response modeling methodology. Submitted for publication.
11 ISO/TR 22514‐4: 2007(E). Statistical methods in process management‐capability and performance‐part 4: process capability estimates and performance measures. 1st ed. Available at: www.iso.org/iso
12 Woodall, WH, Spitzner, DJ, Montgomery, D, Gupta, S. Using control charts to monitor process and product quality profiles. J Qual Technol 2004, 36:309–320.
13 Karian, ZA, Dudewicz, EJ. Fitting Statistical Distributions: The Generalized Lambda Distribution and Generalized Bootstrap Methods
. Boca Raton, FL: CRC Press
14 Karian, ZA, Dudewicz, EJ, eds. Handbook of Fitting Statistical Distributions with R. Boca Raton, FL: Chapman %26 Hall/CRC, Taylor %26 Francis Group
; 2010, 1672.
15 Dudewicz, EJ, Levy, GC, Lienhart, JL, Wehrli, F. Statistical analysis of magnetic resonance imaging data in the normal brain (data, screening, normality, discrimination, variability), and implications for expert statistical programming for ESS™ (the Expert Statistical System). Am J Math Manage Sci 1989, 9:299–359.
16 Bolker, BM. Ecological Models and Data in R
. Princeton, NJ: Princeton University Press
; 2008, 396.
17 Benson‐Karhi, D, Shore, H, Shacham, M. Modeling temperature‐dependent properties of water via response modeling methodology (RMM) and comparison with acceptable models. Ind Eng Chem Res 2007, 46:3446–3463.
18 Shore, H, Benson‐Karhi, D. Modeling temperature‐dependent properties of oxygen, argon and nitrogen via response modeling methodology (RMM) and comparison with acceptable models. Ind Eng Chem Res 2010, 49:9469–9485.
19 Shacham, M, Brauner, N, Shore, H, Benson‐Karhi, D. Predicting temperature‐dependent properties by correlations based on similarity of molecular structures: application to liquid density. Ind Eng Chem Res 2008, 47:4496–4504.
20 Ladany, S, Shore, H. Profit maximizing warranty period with sales expressed by a demand function. Qual Reliab Eng Int 2007, 23:291–301.