Strathprints logo
Strathprints Home | Open Access | Browse | Search | User area | Copyright | Help | Library Home | SUPrimo

Merging expert and empirical data for rare event frequency estimation : pool homogenisation for empirical Bayes models

Quigley, John and Hardman, Gavin and Bedford, Tim and Walls, Lesley (2011) Merging expert and empirical data for rare event frequency estimation : pool homogenisation for empirical Bayes models. Reliability Engineering and System Safety, 96 (6). pp. 687-695. ISSN 0951-8320

PDF (Quigley-etalRESS2011-merging-expert-and-empirical-data-for-rare)
Merging_Expert_and_Empirical_Data_for_Rare_Event_Frequency_Estimation.pdf - Submitted Version

Download (749kB) | Preview


Empirical Bayes provides one approach to estimating the frequency of rare events as a weighted average of the frequencies of an event and a pool of events. The pool will draw upon, for example, events with similar precursors. The higher the degree of homogeneity of the pool, then the Empirical Bayes estimator will be more accurate. We propose and evaluate a new method using homogenisation factors under the assumption that events are generated from a Homogeneous Poisson Process. The homogenisation factors are scaling constants, which can be elicited through structured expert judgement and used to align the frequencies of different events, hence homogenising the pool. The estimation error relative to the homogeneity of the pool is examined theoretically indicating that reduced error is associated with larger pool homogeneity. The effects of misspecified expert assessments of the homogenisation factors are examined theoretically and through simulation experiments. Our results show that the proposed Empirical Bayes method using homogenisation factors is robust under different degrees of misspecification.

Item type: Article
ID code: 27910
Keywords: poisson processes, empirical bayes, pairwise comparison, PRA , Risk Management, Applied Mathematics, Industrial and Manufacturing Engineering, Safety, Risk, Reliability and Quality
Subjects: Social Sciences > Industries. Land use. Labor > Risk Management
Department: Strathclyde Business School > Management Science
Depositing user: Mrs Caroline Sisi
Date Deposited: 11 Apr 2011 15:28
Last modified: 12 Feb 2016 07:46
Related URLs:

Actions (login required)

View Item View Item