Risk Analysis with Reference Class Forecasting Adopting Tolerance Regions

  • Vasilios ZarikasEmail author
  • Christos P. Kitsos
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 136)


The target of this paper is to demonstrate the benefits of using tolerance regions statistics in risk analysis. In particular, adopting the expected beta content tolerance regions as an alternative approach for choosing the optimal order of a response polynomial it is possible to improve results in reference class forecasting methodology. Reference class forecasting tries to predict the result of a planned action based on actual outcomes in a reference class of similar actions to that being forecast. Scientists/analysts do not usually work with a best fitting polynomial according to a prediction criterion. The present paper proposes an algorithm, which selects the best response polynomial, as far as a future prediction is concerned for reference class forecasting. The computational approach adopted is discussed with the help of an example of a relevant application.


Risk analysis Reference class forecasting General linear regression Predictive models Tolerance regions 



We would like to thank the referees for the improvement of the English as well for their valuable comments which help us to improve the paper. V. Zarikas acknowledges the support of research funding from ATEI of Central Greece.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Electrical EngineeringAcademic Institute of Technology of Central Greece, ATEI of Central GreeceLamiaGreece
  2. 2.Department of Computer ScienceTechnological Educational Institute of AthensAthensGreece

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