IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v167y2017icp351-361.html
   My bibliography  Save this article

Prior elicitation for Bayesian generalised linear models with application to risk control option assessment

Author

Listed:
  • Hosack, Geoffrey R.
  • Hayes, Keith R.
  • Barry, Simon C.

Abstract

A pragmatic approach to prior elicitation was developed to elicit the parameters and model structure for Bayesian generalised linear models. Predictive elicitation of subjective probability distributions was used to evaluate Risk Control Option (RCO) effectiveness for reducing the risk of ship collisions in Australia’s Territorial Sea and Exclusive Economic Zone. The RCOs considered were pilotage, Vessel Traffic Services (VTS) and Ships’ Routeing Systems (SRS). Predictive relationships with key covariates were documented. Distance from the Territorial Sea Baseline was important for all RCOs, and aggregate measures of shipping traffic patterns such as volume and the distribution of course over ground headings were related to the effectiveness of both VTS and SRS. A synergistic interaction between pilotage and VTS effectiveness was predicted. The elicitation method enabled a practical approach to eliciting subjective probability distributions while accounting for the complexity and myriad factors that contribute to challenging problems. The approach supports coherent updating given new information, and so can be used to support evidence based decision making.

Suggested Citation

  • Hosack, Geoffrey R. & Hayes, Keith R. & Barry, Simon C., 2017. "Prior elicitation for Bayesian generalised linear models with application to risk control option assessment," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 351-361.
    • Handle: RePEc:eee:reensy:v:167:y:2017:i:c:p:351-361
    DOI: 10.1016/j.ress.2017.06.011
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832016301387
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2017.06.011?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. repec:cup:judgdm:v:8:y:2013:i:1:p:25-28 is not listed on IDEAS
    2. Unknown, 2000. "Back Matter," Food Review/ National Food Review, United States Department of Agriculture, Economic Research Service, vol. 23(1), pages 1-2.
    3. Trucco, P. & Cagno, E. & Ruggeri, F. & Grande, O., 2008. "A Bayesian Belief Network modelling of organisational factors in risk analysis: A case study in maritime transportation," Reliability Engineering and System Safety, Elsevier, vol. 93(6), pages 845-856.
    4. Vander Hoorn, Stephen & Knapp, Sabine, 2015. "A multi-layered risk exposure assessment approach for the shipping industry," Transportation Research Part A: Policy and Practice, Elsevier, vol. 78(C), pages 21-33.
    5. Shafiqah Al-Awadhi & Paul Garthwaite, 2006. "Quantifying expert opinion for modelling fauna habitat distributions," Computational Statistics, Springer, vol. 21(1), pages 121-140, March.
    6. Unknown, 2001. "Back Matter," SS-AAEA Journal of Agricultural Economics, Agricultural and Applied Economics Association, pages 1-4.
    7. Paul H. Garthwaite & Shafeeqah A. Al-Awadhi & Fadlalla G. Elfadaly & David J. Jenkinson, 2013. "Prior distribution elicitation for generalized linear and piecewise-linear models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(1), pages 59-75, January.
    8. Garthwaite, Paul H. & Kadane, Joseph B. & O'Hagan, Anthony, 2005. "Statistical Methods for Eliciting Probability Distributions," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 680-701, June.
    9. Szwed, P. & Dorp, J. Rene van & Merrick, J.R.W. & Mazzuchi, T.A. & Singh, A., 2006. "A Bayesian paired comparison approach for relative accident probability assessment with covariate information," European Journal of Operational Research, Elsevier, vol. 169(1), pages 157-177, February.
    10. Unknown, 2000. "Back Matter," Food Review/ National Food Review, United States Department of Agriculture, Economic Research Service, vol. 23(2), pages 1-2.
    11. Vanem, Erik & Antão, Pedro & Østvik, Ivan & de Comas, Francisco Del Castillo, 2008. "Analysing the risk of LNG carrier operations," Reliability Engineering and System Safety, Elsevier, vol. 93(9), pages 1328-1344.
    12. Mary Kynn, 2008. "The ‘heuristics and biases’ bias in expert elicitation," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(1), pages 239-264, January.
    13. Garthwaite, Paul H., 1989. "Fractile assessments for a linear regression model: An experimental study," Organizational Behavior and Human Decision Processes, Elsevier, vol. 43(2), pages 188-206, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Xiang’en Bai & Tian Guan & Xiaofeng Xu & Yingjie Xiao, 2022. "Data Analysis and Decision on Navigation Safety of Yangshan Port Channel," Sustainability, MDPI, vol. 14(13), pages 1-20, June.
    2. Zhang, Mingyang & Zhang, Di & Fu, Shanshan & Kujala, Pentti & Hirdaris, Spyros, 2022. "A predictive analytics method for maritime traffic flow complexity estimation in inland waterways," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    3. Rafael Schwarzenegger & John Quigley & Lesley Walls, 2023. "Is eliciting dependency worth the effort? A study for the multivariate Poisson-Gamma probability model," Journal of Risk and Reliability, , vol. 237(5), pages 858-867, October.
    4. Yu-Fang Chien & Haiming Zhou & Timothy Hanson & Theodore Lystig, 2023. "Informative g -Priors for Mixed Models," Stats, MDPI, vol. 6(1), pages 1-23, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ward, Eric J., 2008. "A review and comparison of four commonly used Bayesian and maximum likelihood model selection tools," Ecological Modelling, Elsevier, vol. 211(1), pages 1-10.
    2. Dencker Peter & Liese Friedrich, 2004. "Local maximin properties of tests in Gaussian shift experiments," Statistics & Risk Modeling, De Gruyter, vol. 22(2), pages 83-108, February.
    3. Bauder, David & Bodnar, Taras & Parolya, Nestor & Schmid, Wolfgang, 2020. "Bayesian inference of the multi-period optimal portfolio for an exponential utility," Journal of Multivariate Analysis, Elsevier, vol. 175(C).
    4. Le, Tri & Clarke, Bertrand, 2016. "Using the Bayesian Shtarkov solution for predictions," Computational Statistics & Data Analysis, Elsevier, vol. 104(C), pages 183-196.
    5. Alexander Karlsson & Björn Hammarfelt & H. Joe Steinhauer & Göran Falkman & Nasrine Olson & Gustaf Nelhans & Jan Nolin, 2015. "Modeling uncertainty in bibliometrics and information retrieval: an information fusion approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(3), pages 2255-2274, March.
    6. McGree, J.M., 2017. "Developments of the total entropy utility function for the dual purpose of model discrimination and parameter estimation in Bayesian design," Computational Statistics & Data Analysis, Elsevier, vol. 113(C), pages 207-225.
    7. Werner, Christoph & Bedford, Tim & Cooke, Roger M. & Hanea, Anca M. & Morales-Nápoles, Oswaldo, 2017. "Expert judgement for dependence in probabilistic modelling: A systematic literature review and future research directions," European Journal of Operational Research, Elsevier, vol. 258(3), pages 801-819.
    8. Gino J. Lim & Jaeyoung Cho & Selim Bora & Taofeek Biobaku & Hamid Parsaei, 2018. "Models and computational algorithms for maritime risk analysis: a review," Annals of Operations Research, Springer, vol. 271(2), pages 765-786, December.
    9. Martins, Marcelo Ramos & Maturana, Marcos Coelho, 2013. "Application of Bayesian Belief networks to the human reliability analysis of an oil tanker operation focusing on collision accidents," Reliability Engineering and System Safety, Elsevier, vol. 110(C), pages 89-109.
    10. HÃ¥vold, Jon Ivar, 2010. "Safety culture and safety management aboard tankers," Reliability Engineering and System Safety, Elsevier, vol. 95(5), pages 511-519.
    11. A Zuashkiani & D Banjevic & A K S Jardine, 2009. "Estimating parameters of proportional hazards model based on expert knowledge and statistical data," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(12), pages 1621-1636, December.
    12. Suyi Li & Qiang Meng & Xiaobo Qu, 2012. "An Overview of Maritime Waterway Quantitative Risk Assessment Models," Risk Analysis, John Wiley & Sons, vol. 32(3), pages 496-512, March.
    13. Siqi Wang & Jingbo Yin & Rafi Ullah Khan, 2020. "The Multi-State Maritime Transportation System Risk Assessment and Safety Analysis," Sustainability, MDPI, vol. 12(14), pages 1-18, July.
    14. Gneiting, Tilmann, 2011. "Making and Evaluating Point Forecasts," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 746-762.
    15. Laura C. Dawkins & Daniel B. Williamson & Stewart W. Barr & Sally R. Lampkin, 2020. "‘What drives commuter behaviour?': a Bayesian clustering approach for understanding opposing behaviours in social surveys," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(1), pages 251-280, January.
    16. Zhaolin Hu & Jing Cao & L. Jeff Hong, 2012. "Robust Simulation of Global Warming Policies Using the DICE Model," Management Science, INFORMS, vol. 58(12), pages 2190-2206, December.
    17. Mkrtchyan, L. & Podofillini, L. & Dang, V.N., 2015. "Bayesian belief networks for human reliability analysis: A review of applications and gaps," Reliability Engineering and System Safety, Elsevier, vol. 139(C), pages 1-16.
    18. Yu, Qing & Teixeira, Ângelo Palos & Liu, Kezhong & Rong, Hao & Guedes Soares, Carlos, 2021. "An integrated dynamic ship risk model based on Bayesian Networks and Evidential Reasoning," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    19. Fadlalla G. Elfadaly & Paul H. Garthwaite, 2020. "On quantifying expert opinion about multinomial models that contain covariates," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 959-981, June.
    20. Fry, John & Binner, Jane M., 2016. "Elementary modelling and behavioural analysis for emergency evacuations using social media," European Journal of Operational Research, Elsevier, vol. 249(3), pages 1014-1023.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:reensy:v:167:y:2017:i:c:p:351-361. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.