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Hybrid correlated data in risk assessment

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  • Bogdan Rębiasz

Abstract

A method for evaluating the risks in a situation has been presented where parameters in the calculation are expressed in the form of dependent fuzzy numbers and probability distributions. The procedure of risk estimation combines stochastic simulation with the execution of arithmetic operations on interactive fuzzy numbers. In order to define operations on such numbers, non-linear programming is used. Relations between the parameters presented in the form of fuzzy numbers and probability distributions are expressed by means of interval regression. The results of computations indicate that the relations between parameters have a significant impact on the ratios characterizing risk.

Suggested Citation

  • Bogdan Rębiasz, 2015. "Hybrid correlated data in risk assessment," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 25(1), pages 81-101.
    • Handle: RePEc:wut:journl:v:1:y:2015:p:81-101:id:1126
    DOI: 10.5277/ord150105
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    References listed on IDEAS

    as
    1. Lee, Haekwan & Tanaka, Hideo, 1999. "Upper and lower approximation models in interval regression using regression quantile techniques," European Journal of Operational Research, Elsevier, vol. 116(3), pages 653-666, August.
    2. J. Arlin Cooper & Scott Ferson & Lev Ginzburg, 1996. "Hybrid Processing of Stochastic and Subjective Uncertainty Data," Risk Analysis, John Wiley & Sons, vol. 16(6), pages 785-791, December.
    Full references (including those not matched with items on IDEAS)

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