Response modeling methodology: Empirical modeling for engineering and science

Response Modeling Methodology: Empirical Modeling For Engineering And Science

Volumn , Issue , 2005, Pages 1-435

  Shore, Haim ^a  

^a BEN GURION UNIVERSITY OF THE NEGEV   (Israel)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 33750103350     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1142/5708     Document Type: Book

References (304)

1. Comment: The references below are an updated list of publications, which address RMM and its derivatives
2. Shore, H. (2000a). General control charts for variables. International Journal of Production Research, 38(8), 1875-1897.
3. Shore, H. (2000b). General control charts for attributes. IIE Transactions (Quality and Reliability Engineering), 32(12), 634-637.
4. Shore, H. (2000c). Three approaches to analyze quality data originating in nonnormal populations. Quality Engineering, 13(2), 277-291.
5. Shore, H. (2001a). Modeling a non-normal response for quality improvement. International Journal of Production Research, 39(17), 4049-4063.
6. Shore, H. (2001b). Inverse normalizing transformations and a normalizing transformation. In. Advances in Methodological and Applied Aspects of Probability and Statistics., Book, N. Balakrishnan (Ed.), Gordon and Breach Science Publishers. Canada. V. 2, 131-146
7. Shore, H. (2002a). Modeling a response with self-generated and externally-generated sources of variation. Quality Engineering, 14(4), 563-578.
8. Shore, H. (2002b). Response Modeling Methodology (RMM)-Exploring the implied error distribution. Communications in Statistics (Theory and Methods), 31(12), 2225-2249.
9. Shore, H., Brauner, N., and Shacham, M. (2002). Modeling physical and thermodynamic properties via inverse normalizing transformations. Industrial and Engineering Chemistry Research 41, 651-656.
10. Shore, H. (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.
11. Shore, H. (2004a). Response Modeling Methodology (RMM)-Validating evidence from engineering and the sciences. Quality and Reliability Engineering International 20, 61-79.
12. Shore, H. (2004b). The Random Fatigue-Limit Model as a special case of the RMM model-A comment on Pascual (2003a). Communications in Statistics (Simulation and Computation), , 33(2), 537-539.
13. Shore, H. (2004c) Response Modeling Methodology (RMM)-Current distributions, transformations, and approximations as special cases of the RMM error distribution. Communications in Statistics (Theory and Methods), , 33(7), 1491-1510.
14. Shore, H. (2004d). Response Modeling Methodology (RMM)- Maximum likelihood estimation procedures. Computational Statistics and Data Analysis. In press.
15. Shore, H. (2005). Accurate RMM-based approximations for the CDF of the normal distribution. Communications in Statistics (Theory and Methods), 34(3). In press.
16. Clements, J. A. (1989). Process capability calculations for non-normal distributions. Quality Progress, , 22(2), 49-55.
17. Grimshaw, S. D., Alt, F. B. (1997). Control charts for quantile function values. Journal of Quality Technology, , 24(1), 1-7.
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19. 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. Communications in Statistics (Simulation and Computation), B8(3), 191-229.
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51. Myers, R. H., Montgomery, D. C., Vining, G. G. (2002). Generalized Linear Models, with Applications in Engineering and the Sciences. John Wiley & Sons.
52. Shore, H. (2004). Response Modeling Methodology (RMM)- Current distributions, transformations and approximations as special cases of the RMM error distribution. Communications in Statistics (Theory and Methods), 33(7), 1491-1510.
53. Smith, D. M. (2003). Computing single parameter transformations. Communications in Statistics (Simulation and Computation), 32(3), 605-618.
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64. Chou, Y.M, Polansky, A. M., Mason, R. L. (1998). Transforming non-normal data to normality in statistical process control. Journal of Quality Technology, 30(2), 133-141.
65. Clements, J. A. (1989). Process capability calculations for non-normal distributions. Quality Progress, , 22(2), 49-55.
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69. Grimshaw, S. D., Alt, F. B. (1997). Control charts for quantile-function values. Journal of Quality Technology, 24(1), 1-7.
70. Hoaglin, D. C., Mosteller, F., Tukey, J. W. (1985). Explorative Data, Tables, Trends, and Shapes. John Wiley & Sons.
71. Johnson, N. L. (1949). Systems of frequency curves generated by methods of translation. Biometrica 36, 149-176.
72. Johnson, N. L. (1954) Systems of frequency curves derived from the first law of Laplace. Trabajos de Estadistica 5, 283-291.
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90. Shore, H. (1986a). Simple general approximations for a random variable and its inverse distribution function based on linear transformations of a non skewed variate. SIAM J. Sci. Stat. Comput., 7(1), 1-23.
91. Shore, H. (1986b). An approximation for the inverse distribution function of a combination of random variables, with an application to operating theaters. Journal of Statistical Computation and Simulation, 23(3), 157-181.
92. Shore, H. (1995). Fitting a distribution by the first two sample moments (partial and complete). Computational Statistics and Data Analysis 19, 563-577.
93. Shore, H. (1996). A new estimate of skewness with MSE smaller than that of the sample skewness. Communications in Statistics (Simulation and Computation), 25(2), 403-414.
94. Shore, H. (1998a). Approximating an unknown distribution when distribution information is extremely limited. Communications in Statistics (Simulation and Computation), 27(2), 501-523.
95. Shore, H. (1998b). 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.
96. Shore, H. (2000a). Three approaches to analyze quality data originating in nonnormal populations. Quality Engineering, 13(2), 277-291.
97. Shore, H. (2000b). General control charts for attributes. IIE Transactions (Quality and Reliability Engineering), 32(12), 634-637.
98. Shore, H. (2004). Non-normal populations in quality applications- A revisited perspective. Quality and Reliability Engineering International, , 20(4), 375-382.
99. Stuart, A., Ord, J. K. (1987) Kendall.s Advanced Theory of Statistics. V. 1:Distribution Theory. Charles Griffin & Company, Ltd., London.
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107. Cohen, A. C., Whitten, B. J. (1988). Parameter Estimation in Reliability and Life Span Models. Marcel Dekker, New-York.
108. Clements, J. A. (1989). Process capability calculations for non-normal distributions. Quality Progress, 22(2), 49-55.
109. Draper, N. R., Smith, H. (1998). Applied Regression Analysis. 3rd Ed., John Wiley & Sons.
110. Johnson, N. L., Kotz, S., Balakrishnan, N. (1994). Continuous Univariate Distributions. V. 1. 2nd Edition. John Wiley & Sons.
111. Karian, Z. A., Dudewicz, E. J. (2002). Fitting Statistical Distributions- The Generalized Lambda Distribution and Generalized Bootstrap Methods. Chapman & Hall/CRC Press. London.
112. Kotz, S., Johnson, N. L. (2002). Process capability indices- A review, 1992-2000, Journal of Quality Technology, 34, 2-19.
113. Myers, R. H., Montgomery, D. C., Vining, G. G. (2002). Generalized Linear Models, with Applications in Engineering and the Sciences. John Wiley & Sons.
114. 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. Communications in Statistics (Simulation and Computation), B8(3), 191-229.
115. Shore, H. (2002). Response Modeling Methodology (RMM)- Exploring the implied error distribution. Communications in Statistics (Theory and Methods), 31(12), 2225-2249.
116. Shore, H. (2004a). Response Modeling Methodology (RMM)- Validating evidence from engineering and the sciences. Quality and Reliability Engineering International, 20, 61-79.
117. Shore, H. (2004b). Non-normal populations in quality applications- A revisited perspective. Quality and Reliability Engineering International, 20, 375-382.
118. Shore, H. (2004c). Response Modeling Methodology (RMM)- Current distributions, transformations and approximations as special cases of RMM error distribution. Communications in Statistics (Theory and Methods), , 33(7), 1491-1510.
119. Stuart, A., Ord, J. K. (1987). Kendall.s Advanced Theory of Statistics. V. 1:Distribution Theory. Charles Griffin & Company, Ltd., London.
120. Johnson, N. L., Kotz, S., Balakrishnan, N. (1994). Continuous Univariate Distributions. Volume 1. 2nd Edition. John Wiley & Sons.
121. Shore, H. (2002). Modeling a response with self-generated and externally-generated sources of variation. Quality Engineering, , 14(4), 563-578.
122. Stuart, A., Ord, J. K. (1987). Kendall.s Advanced Theory of Statistics. V. 1:Distribution Theory. Charles Griffin & Company, London.
123. Condra, L. W. (1993). Reliability Improvement with Design of Experiments. Marcel Dekker, NY.
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127. Myers, R. H., Montgomery, D. C., Vining, G. G. (2002). Generalized Linear Models, with Applications in Engineering and the Sciences. John Wiley & Sons.
128. Shore, H. (2001). Modeling a non-normal response for quality improvement. International Journal of Production Research, 39(17), 4049-4063.
129. Shore, H. (2002). Modeling a response with self-generated and externally-generated sources of variation. Quality Engineering, , 14(4), 563-578.
130. Shore, H. (2004a). Response Modeling Methodology (RMM)- Current statistical distributions, transformations and approximations as special cases of RMM. Communications in Statistics (Theory and Methods), 33(7), 1491-1510.
131. Shore, H. (2004b). Response Modeling Methodology (RMM)- Validating evidence from engineering and the sciences. Quality and Reliability Engineering International 20, 61-79.
132. Shore, H. (2004c). The Random Fatigue-Limit Model as a special case of the RMM model- a comment on Pascual (2003a). Communications in Statistics (Simulation and Computation), 33(2), 537-539.
133. Shore, H. (2004d). Response Modeling Methodology (RMM)- Maximum likelihood estimation procedures. Computational Statistics and Data Analysis. In press.
134. Wedderburn, R. W. M. (1974). Quasi-Likelihood functions, generalized linear models and the Gauss Newton method. Biometrics, 44, 121-130.
135. Johnson, N. L., Kotz, S., Balakrishnan, N. (1994). Continuous Univariate Distributions. Volume 1. 2nd Edition. John Wiley & Sons.
136. Shore, H. (2000). General control charts for variables. International Journal of Production Research, 38(8), 1875-1897.
137. Shore, H. (2002). Response Modeling Methodology (RMM)- Exploring the implied error distribution. Communications in Statistics (Theory and Methods), 31(12), 2225-2249.
138. Stuart, A., Ord, J. K. (1987). Kendall.s Advanced Theory of Statistics. V. 1:Distribution Theory. Charles Griffin & Company, Ltd., London.
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141. Clements, J. A. (1989). Process capability calculations for non-normal distributions. Quality Progress, , 22(2), 49-55.
142. Karian, Z. A., Dudewicz, E. J. (2000). Fitting Statistical Distributions: The Generalized Lambda Distribution and Generalized Bootstrap Methods. CRC Press, Boca Raton, Florida, USA.
143. 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. Communications in Statistics (Simulation and Computation), B8(3), 191-229.
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154. Shore, H. (2004). Non-normal populations in quality applications- A revisited perspective. Quality and Reliability Engineering International, 20(4), 375-382.
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161. Johnson, N. L. (1954). Systems of frequency curves derived from the first law of Laplace. Trabajos de Estadistica 5, 283-291.
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166. Shore, H. (2001). Modeling a non-normal response for quality improvement. International Journal of Production Research, , 39(17), 4049-4063.
167. Shore, H. (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.
168. Shore, H. (2004). Response Modeling Methodology (RMM)- Current distributions, transformations and approximations as special cases of the RMM error distribution. Communications in Statistics (Theory and Methods), , 33(7), 1491-1510.
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177. Shore, H. (2002). Response Modeling Methodology (RMM)- Exploring the implied error distribution. Communications in Statistics (Theory and Methods), , 31(12), 2225-2249.
178. Shore, H. (2004a). Response Modeling Methodology (RMM)- Validating evidence from engineering and the sciences. Quality and Reliability Engineering International 20, 61-79.
179. Shore, H. (2004b). Response Modeling Methodology (RMM)- Current distributions, transformations and approximations as special cases of the RMM error distribution. Communications in Statistics (Theory and Methods), , 33(7), 1491-1510.
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298. Shore, H. (1995b). Fitting a distribution by the first two moments (partial and complete). Journal of Computational Statistics and Data Analysis, 19, 563-577.
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301. Shore, H. (1998). Approximating an unknown distribution when distribution information is extremely limited. Communication in Statistics (Simulation and Computation), , 27(2), 501-523.
302. Shore, H. (1999). Optimal solutions for stochastic inventory models when the leadtime demand distribution is partially specified. International Journal of Production Economics 59, 477-485.
303. Shore, H. (2004a). A general solution for the newsboy model with random order size and possibly a cutoff transaction size. Journal of the Operational Research Society, , 55(11), 1218-1228.
304. Shore, H. (2004b). Non-normal populations in quality applications- A revisited perspective. Quality and Reliability Engineering International, , 20(4), 375-382.