Risk analysis with reference class forecasting adopting tolerance regions

Vasilios Zarikas, Christos P. Kitsos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationTheory and Practice of Risk Assessment - ICRA5 2013
EditorsAlexandros Rigas, Christos P. Kitsos, Sneh Gulati, Teresa A. Oliveira
PublisherSpringer New York
Pages235-247
Number of pages13
ISBN (Print)9783319180281
DOIs
Publication statusPublished - Jan 1 2015
Event5th International Conference on Risk Analysis, ICRA5 2013 - Tomar, Portugal
Duration: May 30 2013Jun 1 2013

Publication series

NameSpringer Proceedings in Mathematics and Statistics
Volume136
ISSN (Print)2194-1009
ISSN (Electronic)2194-1017

Other

Other5th International Conference on Risk Analysis, ICRA5 2013
CountryPortugal
CityTomar
Period5/30/136/1/13

Keywords

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

ASJC Scopus subject areas

  • Mathematics(all)

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  • Cite this

    Zarikas, V., & Kitsos, C. P. (2015). Risk analysis with reference class forecasting adopting tolerance regions. In A. Rigas, C. P. Kitsos, S. Gulati, & T. A. Oliveira (Eds.), Theory and Practice of Risk Assessment - ICRA5 2013 (pp. 235-247). (Springer Proceedings in Mathematics and Statistics; Vol. 136). Springer New York. https://doi.org/10.1007/978-3-319-18029-8_18