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Selection of Survival Models for Technological Development  

Oh, H.S. (Department of Industrial and Management Engineering, Hannam University)
Kim, C.S. (Department of Industrial and Management Engineering, Hannam University)
Rhee, H.K. (Department of Industrial and Management Engineering, Hannam University)
Yim, D.S. (Department of Industrial and Management Engineering, Hannam University)
Cho, J.H. (Department of Industrial Engineering, Kumoh Institute of Technology)
Publication Information
Journal of Korean Society of Industrial and Systems Engineering / v.32, no.4, 2009 , pp. 184-191 More about this Journal
Abstract
In a technological driven environment, a depreciation estimate which is based on traditional life analysis results in a decelerated rate of capital recovery. This time pattern of technological growths models needs to be incorporated into life analysis framework especially in those industries experiencing fast technological changes. The approximation technique for calculating the variance can be applied to the six growth models that were selected by the degree of skewness and the transformation of the functions. For the Pearl growth model, the Gompertz growth model, and the Weibull growth model, the errors have zero mean and a constant variance over time. However, transformed models like the linearized Fisher-Pry model, the linearized Gompertz growth model, and the linearized Weibull growth model have increasing variance from zero to that point at which inflection occurs. It can be recommended that if the variance of error over time is increasing, then a transformation of observed data is appropriate.
Keywords
Life analysis; Technological Growth Models;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
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1 Lakani, H.; “Diffusion of Environment-Saving Technological Change：A Petroleum Refining Case Study,” Technological Forecasting and Social Change, 7(1)：33-35. 1975   DOI   ScienceOn
2 Oh, H. S., Yim, D. S. and Moon, G. J.; “Error Structure of Technological Growth Models,” Journal of the Korean Society for Quality Management, 23(1)：95-105, 1995
3 Oh, H. S., Kim, H. K., Rhee and K. T., Kim.; “A Study on the Application of Mixed Weibull Function to Estimate Survivor Curves of Industrial Property,” Journal of Society of Korea Industrial and SystemsEngineering, 30(1)：66-73, 2007   과학기술학회마을
4 Oh, H. S., Kim, C. S., Suh, J. Y. and Cho, J. H.; “A Study on the Estimation of Economic Service Life on Semiconductor Equipment,” Journal of Society of Korea Industrial and Systems Engineering, 30(4)：164-169, 2007   ScienceOn
5 Oh, H. S., Kim, C. S., Rhee, H. K., and Cho, J. H.; 'A Study on the Estimation of Depreciation Rate on Petrochemical Equipment,' Journal of Society of Korea Industrial and Systems Engineering, 32(1)：130-136, 2009   ScienceOn
6 Sharif, M. N. and Uddin, G. A.; “A Procedure for Adapting Technological Forecasting Models,” Technological Forecasting and Social Change, 7：99-106. 1975   DOI   ScienceOn
7 Hayes, J. G.; Numerical Approximation to Functions and Data, University of London, The Athlone Press, 1970
8 Dandekar, M.; “Investigation the Product Life Cycle Concepts：An Application to Capital Recovery, Evaluation within the Telephone Industry,” Ph.D. Dissertation, Iowa State University of Science and Technology, Ames, Iowa, U. S. A., 1987
9 Winfrey, R.,; Statistical Analysis of Industrial Property Retirement, Revised edition：ERI Bulletin 125, Iowa State University of Science and Technology, Ames, Iowa, U.S.A., 1967
10 Oh, H. S. and Moon, G. J.; “A Comparison of Technological Growth Models,” Journal of the Korean Society for Quality Management, 22(2)：51-68. 1994
11 Oh, H. S.; “The Selection of Technological Forecasting Models in Life Analysis,” Ph.D. Dissertation, Iowa State University of Science and Technology, Ames, Iowa, U.S.A., 1988
12 Sharif, M. N. and Islam, M. N.; “The Weibull distribution as a General Model for Forecasting Technological Change,” Technological Forecasting and Social Change, 18：247-256, 1980   DOI   ScienceOn
13 Fisher, J. C. and Pry, R. H.; “A Simple Substitution Model of Technological Change,” Technological Forecasting and Social Change, 3：75-88, 1971   DOI   ScienceOn
14 Tingyan, X.; “A Combined Growth Model for Trend Forecasts,” Technological Forecasting and Social Change, 8：175-186, 1990   DOI   ScienceOn
15 Fitch, J. C.; “Conceptual Framework for Forecasting the Useful Life of Industrial Property,” Proceedings of the Iowa State University Regulatory Conference, Ames, Iowa, U.S.A., 1984
16 Pearl, R.; The Biology of Population, New York：Alfred A. Konpf 1925
17 Booth, H.“Transforming Gompertz's Function for Fertility Analysis：The Development of a Standard for the Relational Gompertz Function,” Population Studies, 38：495-506, 1993
18 Martino, J. P.; Technological Forecasting For Decision Making, Elsevier, New York, U.S.A., 1975
19 Conover, W. J.Practical Non-parametric Statistics, 2nd Edition, John Wiley and Sons, 1980
20 Goldfeld, S. M. and Quandt, R. E.; “Some Test for Homoscedasticity,” Journal of the American Statistical Association, 60(310)：539-547, 1965   DOI   ScienceOn
21 Wolf F.; “Forecasting Force of Mortality,” Proceedings of the Iowa State University Regulatory Conference, Ames, Iowa, 1985
22 Oh, H. S., Kim, C. S. and Cho, J. H.; “Estimation of Retirement Rate on Domestic Industrial Property,” Journal of Society of Korea Industrial and Systems Engineering, 25(4)：79-85, 2002
23 Weibull, W.; “A Statistical Distribution Function of Wide Applicability,” Journal of Applied Mechanics, 18：293-297, 1951
24 White, R. E.; “A Test Procedure for Simulated Plant Record Method of Life Analysis,” Journal of the American Statistical Association, 70：1204-1212, 1970