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 |
|---|---|
| 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 |
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Keywords
- General linear regression
- Predictive models
- Reference class forecasting
- Risk analysis
- Tolerance regions
ASJC Scopus subject areas
- Mathematics(all)
Cite this
Risk analysis with reference class forecasting adopting tolerance regions. / Zarikas, Vasilios; Kitsos, Christos P.
Theory and Practice of Risk Assessment - ICRA5 2013. Vol. 136 Springer New York, 2015. p. 235-247.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
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 -