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
PublisherSpringer New York
Pages235-247
Number of pages13
Volume136
ISBN (Print)9783319180281
DOIs
Publication statusPublished - 2015
Externally publishedYes
Event5th International Conference on Risk Analysis, ICRA5 2013 - Tomar, Portugal
Duration: May 30 2013Jun 1 2013

Other

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

Fingerprint

Risk Analysis
Tolerance
Forecasting
Polynomial
Prediction
Forecast
Statistics
Predict
Target
Class
Methodology
Alternatives
Demonstrate

Keywords

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

ASJC Scopus subject areas

  • Mathematics(all)

Cite this

Zarikas, V., & Kitsos, C. P. (2015). Risk analysis with reference class forecasting adopting tolerance regions. In 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.

Theory and Practice of Risk Assessment - ICRA5 2013. Vol. 136 Springer New York, 2015. p. 235-247.

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

Zarikas, V & Kitsos, CP 2015, Risk analysis with reference class forecasting adopting tolerance regions. in 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
Zarikas V, Kitsos CP. Risk analysis with reference class forecasting adopting tolerance regions. In Theory and Practice of Risk Assessment - ICRA5 2013. Vol. 136. Springer New York. 2015. p. 235-247 https://doi.org/10.1007/978-3-319-18029-8_18
Zarikas, Vasilios ; Kitsos, Christos P. / Risk analysis with reference class forecasting adopting tolerance regions. Theory and Practice of Risk Assessment - ICRA5 2013. Vol. 136 Springer New York, 2015. pp. 235-247
@inproceedings{b045e31e3edc4416917f8204d932dc57,
title = "Risk analysis with reference class forecasting adopting tolerance regions",
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.",
keywords = "General linear regression, Predictive models, Reference class forecasting, Risk analysis, Tolerance regions",
author = "Vasilios Zarikas and Kitsos, {Christos P.}",
year = "2015",
doi = "10.1007/978-3-319-18029-8_18",
language = "English",
isbn = "9783319180281",
volume = "136",
pages = "235--247",
booktitle = "Theory and Practice of Risk Assessment - ICRA5 2013",
publisher = "Springer New York",
address = "United States",

}

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 -