Forecasting S-shaped diffusion processes via response modelling methodology

Journal of the Operational Research Society

Volumn 58, Issue 6, 2007, Pages 720-728

  Shore, H ^a   Benson Karhi, D ^a  

^a BEN GURION UNIVERSITY OF THE NEGEV   (Israel)

Author keywords

Diffusion processes; Forecasting; Inverse normalizing transformations; Nonlinear regression; Response modeling methodology; Sigmoid curves

Indexed keywords

DISTRIBUTION FUNCTIONS; ERROR ANALYSIS; INVERSE PROBLEMS; NONLINEAR ANALYSIS; POPULATION STATISTICS; PRODUCT DESIGN; REGRESSION ANALYSIS;

DIFFUSION PROCESSES; INVERSE NORMALIZING TRANSFORMATIONS; NONLINEAR REGRESSION; RESPONSE MODELLING METHODOLOGY (RMM); SIGMOID CURVES;

OPERATIONS RESEARCH;

EID: 34249105814     PISSN: 01605682     EISSN: 14769360     Source Type: Journal    
DOI: 10.1057/palgrave.jors.2602187     Document Type: Article

References (6)

1. Benson-Karhi D (2001). Technological forecasting using inverse normalizing transformations Unpublished thesis report, Supervised by H Shore. Department of Industrial Engineering, Ben-Gurion University.
2. Meade N and Islam T (1998). Technological forecasting - model selection, model stability and combining models. Mngt Sci 44(8): 1115-1130.
3. Shore H (2000). General control charts for variables. Int J Prod Res 38(8): 1875-1897.
4. Shore H (2004). Response modeling methodology (RMM) - current distributions, transformations and approximations as special cases of the RMM error distribution. Comm Stat (Theory & Methods) 33(7): 1491-1510.
5. Shore H (2005). Response Modeling Methodology - Empirical Modeling for Engineering and Science. World Scientific Publishing Co. Ltd.: Singapore.
6. Young P and Ord JK (1989). Model selection and estimation for technological growth curves. Int J Forecasting 5: 501-513.