### 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 language | English |
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Title of host publication | Theory and Practice of Risk Assessment - ICRA5 2013 |

Publisher | Springer New York |

Pages | 235-247 |

Number of pages | 13 |

Volume | 136 |

ISBN (Print) | 9783319180281 |

DOIs | |

Publication status | Published - 2015 |

Externally published | Yes |

Event | 5th International Conference on Risk Analysis, ICRA5 2013 - Tomar, Portugal Duration: May 30 2013 → Jun 1 2013 |

### Other

Other | 5th International Conference on Risk Analysis, ICRA5 2013 |
---|---|

Country | Portugal |

City | Tomar |

Period | 5/30/13 → 6/1/13 |

### Fingerprint

### Keywords

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

### ASJC Scopus subject areas

- Mathematics(all)

### Cite this

*Theory and Practice of Risk Assessment - ICRA5 2013*(Vol. 136, pp. 235-247). Springer New York. https://doi.org/10.1007/978-3-319-18029-8_18

**Risk analysis with reference class forecasting adopting tolerance regions.** / Zarikas, Vasilios; Kitsos, Christos P.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Theory and Practice of Risk Assessment - ICRA5 2013.*vol. 136, Springer New York, pp. 235-247, 5th International Conference on Risk Analysis, ICRA5 2013, Tomar, Portugal, 5/30/13. https://doi.org/10.1007/978-3-319-18029-8_18

}

TY - GEN

T1 - Risk analysis with reference class forecasting adopting tolerance regions

AU - Zarikas, Vasilios

AU - Kitsos, Christos P.

PY - 2015

Y1 - 2015

N2 - 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.

AB - 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.

KW - General linear regression

KW - Predictive models

KW - Reference class forecasting

KW - Risk analysis

KW - Tolerance regions

UR - http://www.scopus.com/inward/record.url?scp=84946400542&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84946400542&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-18029-8_18

DO - 10.1007/978-3-319-18029-8_18

M3 - Conference contribution

SN - 9783319180281

VL - 136

SP - 235

EP - 247

BT - Theory and Practice of Risk Assessment - ICRA5 2013

PB - Springer New York

ER -